Monthly Archives: August 2022

Companies come in promising the world and often dont deliver, said Bob Wachter, head of the department of medicine at the University of California, San Francisco. When I look for examples of true AI and machine learning thats really making a difference, theyre pretty few and far between. Its pretty underwhelming.

Administrators say algorithms the software that processes data from outside companies dont always work as advertised because each health system has its own technological framework. So hospitals are building out engineering teams and developing artificial intelligence and other technology tailored to their own needs.

But its slow going. Research based on job postings shows health care behind every industry except construction in adopting AI.

The Food and Drug Administration has taken steps to develop a model for evaluating AI, but it is still in its early days. There are questions about how regulators can monitor algorithms as they evolve and rein in the technologys detrimental aspects, such as bias that threaten to exacerbate health care inequities.

Sometimes theres an assumption that AI is working, and its just a matter of adopting it, which is not necessarily true, said Florenta Teodoridis, a professor at the University of Southern Californias business school whose research focuses on AI. She added that being unable to understand why an algorithm came to a certain result is fine for things like predicting the weather. But in health care, its impact is potentially life-changing.

Despite the obstacles, the tech industry is still enthusiastic about AIs potential to transform health care.

The transition is slightly slower than I hoped but well on track for AI to be better than most radiologists at interpreting many different types of medical images by 2026, Hinton told POLITICO via email. He said he never suggested that we should get rid of radiologists, but that we should let AI read scans for them.

If hes right, artificial intelligence will start taking on more of the rote tasks in medicine, giving doctors more time to spend with patients to reach the right diagnosis or develop a comprehensive treatment plan.

I see us moving as a medical community to a better understanding of what it can and cannot do, said Lara Jehi, chief research information officer for the Cleveland Clinic. It is not going to replace radiologists, and it shouldnt replace radiologists.

Radiology is one of the most promising use cases for AI. The Mayo Clinic has a clinical trial evaluating an algorithm that aims to reduce the hours-long process oncologists and physicists undertake to map out a surgical plan for removing complicated head and neck tumors.

An algorithm can do the job in an hour, said John D. Halamka, president of Mayo Clinic Platform: Weve taken 80 percent of the human effort out of it. The technology gives doctors a blueprint they can review and tweak without having to do the basic physics themselves, he said.

NYU Langone Health has also experimented with using AI in radiology. The health system has collaborated with Facebooks Artificial Intelligence Research group to reduce the time it takes to get an MRI from one hour to 15 minutes. Daniel Sodickson, a radiological imaging expert at NYU Langone who worked on the research, sees opportunity in AIs ability to downsize the amount of data doctors need to review.

When I look for examples of true AI and machine learning thats really making a difference, theyre pretty few and far between. Its pretty underwhelming.

Bob Wachter, head of the department of medicine at the University of California, San Francisco

Covid has accelerated AIs development. Throughout the pandemic, health providers and researchers shared data on the disease and anonymized patient data to crowdsource treatments.

Microsoft and Adaptive Biotechnologies, which partner on machine learning to better understand the immune system, put their technology to work on patient data to see how the virus affected the immune system.

The amount of knowledge thats been obtained and the amount of progress has just been really exciting, said Peter Lee, corporate vice president of research and incubations at Microsoft.

There are other success stories. For example, Ochsner Health in Louisiana built an AI model for detecting early signs of sepsis, a life-threatening response to infection. To convince nurses to adopt it, the health system created a response team to monitor the technology for alerts and take action when needed.

Im calling it our care traffic control, said Denise Basow, chief digital officer at Ochsner Health. Since implementation, she said, death from sepsis is declining.

The biggest barrier to the use of artificial intelligence in health care has to do with infrastructure.

Health systems need to enable algorithms to access patient data. Over the last several years, large, well-funded systems have invested in moving their data into the cloud, creating vast data lakes ready to be consumed by artificial intelligence. But thats not as easy for smaller players.

Another problem is that every health system is unique in its technology and the way it treats patients. That means an algorithm may not work as well everywhere.

Over the last year, an independent study on a widely used sepsis detection algorithm from EHR giant Epic showed poor results in real-world settings, suggesting where and how hospitals used the AI mattered.

This quandary has led top health systems to build out their own engineering teams and develop AI in-house.

That could create complications down the road. Unless health systems sell their technology, its unlikely to undergo the type of vetting that commercial software would. That could allow flaws to go unfixed for longer than they might otherwise. Its not just that the health systems are implementing AI while no ones looking. Its also that the stakeholders in artificial intelligence, in health care, technology and government, havent agreed upon standards.

A lack of quality data which gives algorithms material to work with is another significant barrier in rolling out the technology in health care settings.

Over the last several years, large, well-funded systems have invested in moving their data into the cloud, creating vast data lakes ready to be consumed by artificial intelligence.|Elaine Thompson/AP Photo

Much data comes from electronic health records but is often siloed among health care systems, making it more difficult to gather sizable data sets. For example, a hospital may have complete data on one visit, but the rest of a patients medical history is kept elsewhere, making it harder to draw inferences about how to proceed in caring for the patient.

We have pieces and parts, but not the whole, said Aneesh Chopra, who served as the governments chief technology officer under former President Barack Obama and is now president of data company CareJourney.

While some health systems have invested in pulling data from a variety of sources into a single repository, not all hospitals have the resources to do that.

Health care also has strong privacy protections that limit the amount and type of data tech companies can collect, leaving the sector behind others in terms of algorithmic horsepower.

Importantly, not enough strong data on health outcomes is available, making it more difficult for providers to use AI to improve how they treat patients.

That may be changing. A recent series of studies on a sepsis algorithm included copious details on how to use the technology in practice and documented physician adoption rates. Experts have hailed the studies as a good template for how future AI studies should be conducted.

But working with health care data is also more difficult than in other sectors because it is highly individualized.

We found that even internally across our different locations and sites, these models dont have a uniform performance, said Jehi of the Cleveland Clinic.

And the stakes are high if things go wrong. The number of paths that patients can take are very different than the number of paths that I can take when Im on Amazon trying to order a product, Wachter said.

Health experts also worry that algorithms could amplify bias and health care disparities.

For example, a 2019 study found that a hospital algorithm more often pushed white patients toward programs aiming to provide better care than Black patients, even while controlling for the level of sickness.

Last year, the FDA published a set of guidelines for using AI as a medical device, calling for the establishment of good machine learning practices, oversight of how algorithms behave in real-world scenarios and development of research methods for rooting out bias.

The agency subsequently published more specific guidelines on machine learning in radiological devices, requiring companies to outline how the technology is supposed to perform and provide evidence that it works as intended. The FDA has cleared more than 300 AI-enabled devices, largely in radiology, since 1997.

Regulating algorithms is a challenge, particularly given how quickly the technology advances. The FDA is attempting to head that off by requiring companies to institute real-time monitoring and submit plans on future changes.

But in-house AI isnt subject to FDA oversight. Bakul Patel, former head of the FDAs Center for Devices and Radiological Health and now Googles senior director for global digital health strategy and regulatory affairs, said that the FDA is thinking about how it might regulate noncommercial artificial intelligence inside of health systems, but he adds, theres no easy answer.

FDA has to thread the needle between taking enough action to mitigate flaws in algorithms while also not stifling AIs potential, he said.

Some argue that public-private standards for AI would help advance the technology. Groups, including the Coalition for Health AI, whose members include major health systems and universities as well as Google and Microsoft, are working on this approach.

But the standards they envision would be voluntary, which could blunt their impact if not widely adopted.

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Artificial intelligence was supposed to transform health care. It hasn't. - POLITICO

Artificial intelligence can now make convincing images of buildings. Is that a good thing? | Forum | Archinect '); }, imageUploadError: function(json, xhr) { alert(json.message); } }}); /*$(el).ckeditor(function() {}, {//removePlugins: 'elementspath,scayt,menubutton,contextmenu',removePlugins: 'liststyle,tabletools,contextmenu',//plugins:'a11yhelp,basicstyles,bidi,blockquote,button,clipboard,colorbutton,colordialog,dialogadvtab,div,enterkey,entities,filebrowser,find,flash,font,format,forms,horizontalrule,htmldataprocessor,iframe,image,indent,justify,keystrokes,link,list,maximize,newpage,pagebreak,pastefromword,pastetext,popup,preview,print,removeformat,resize,save,smiley,showblocks,showborders,sourcearea,stylescombo,table,specialchar,tab,templates,toolbar,undo,wysiwygarea,wsc,vimeo,youtube',//toolbar: [['Bold', 'Italic', 'BulletedList', 'Link', 'Image', 'Youtube', 'Vimeo' ]],plugins:'a11yhelp,basicstyles,bidi,blockquote,button,clipboard,colorbutton,colordialog,dialogadvtab,div,enterkey,entities,filebrowser,find,flash,font,format,forms,horizontalrule,htmldataprocessor,iframe,image,indent,justify,keystrokes,link,list,maximize,newpage,pagebreak,pastefromword,pastetext,popup,preview,print,removeformat,resize,save,smiley,showblocks,showborders,sourcearea,stylescombo,table,specialchar,tab,templates,toolbar,undo,wysiwygarea,wsc,archinect',toolbar: [['Bold', 'Italic', 'BulletedList','NumberedList', 'Link', 'Image']],resize_dir: 'vertical',resize_enabled: false,//disableObjectResizing: true,forcePasteAsPlainText: true,disableNativeSpellChecker: false,scayt_autoStartup: false,skin: 'v2',height: 300,linkShowAdvancedTab: false,linkShowTargetTab: false,language: 'en',customConfig : '',toolbarCanCollapse: false });*/ }function arc_editor_feature(el) { $(el).redactor({minHeight: 300,pasteBlockTags: ['ul', 'ol', 'li', 'p'],pasteInlineTags: ['strong', 'br', 'b', 'em', 'i'],imageUpload: '/redactor/upload',plugins: ['source', 'imagemanager'],buttons: ['html', 'format', 'bold', 'italic', 'underline', 'lists', 'link', 'image'],formatting: ['p'],formattingAdd: {"figcaption": {title: 'Caption',args: ['p', 'class', 'figcaption', 'toggle']},"subheading": {title: 'Subheading',args: ['h3', 'class', 'subheading', 'toggle']},"pullquote-left": {title: 'Quote Left',args: ['blockquote', 'class', 'pullquote-left', 'toggle']},"pullquote-centered": {title: 'Quote Centered',args: ['blockquote', 'class', 'pullquote-center', 'toggle']},"pullquote-right": {title: 'Quote Right',args: ['blockquote', 'class', 'pullquote-right', 'toggle']},"chat-question": {title: 'Chat Question',args: ['p', 'class', 'chat-question', 'toggle']}, "chat-answer": {title: 'Chat Answer',args: ['p', 'class', 'chat-answer', 'toggle']}, },callbacks:{ imageUpload: function(image, json) { $(image).replaceWith('

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Artificial intelligence can now make convincing images of buildings. Is that a good thing? - Archinect

Inventions such as the wheel, the printing press, light bulb, telescope, microscope, transistor, microchip, and the Internet, are amazing in and of themselves. However, these, and thousands of other inventions have also provided an indispensable foundation, and a toolkit, for other, newer inventions, leading to a pace of innovative progress unlike anything seen before. For example, the microchip, leading to the computer, has helped humans conceive of and find new inventions by helping them process information more efficiently. But the computer, until recently, has only helped to solve inventive problems framed by humans and arrive at solutions that are, in some sense, only anticipated by humans. Until now, prior inventions have only provided assistance to the inventive activity of human beings; historically, the human mind has ultimately been the source of invention.

That paradigm, however, is changing. Recent advances in computer technology, as well as the exponential growth in available data, are leading to the advent of artificial intelligence and machine learning. Some have said that most of the data ever created has been created in the last several years. What we call artificial intelligence represents a massive increase in the power of computer problem solving that has been enabled by massive amounts of new data. Data is like fuel the more data available to computer algorithms, the more powerful those algorithms become in operations that approach machine learning. And, with this new power, machines are becoming increasingly able to formulate problems and imagine (i.e., invent) solutions in ways that were previously reserved for human beings.

The possibility that a machine can be an inventor raises interesting questions for how we think about incentivizing inventorship and the kinds of monopolistic protection we afford to inventions in the future. Patent law is the body of law that deals with, and specifically, provides certain protections for, inventions. The concept of inventorship is core to patent law, and, with the change in the inventorship paradigm noted above, the question naturally arises who, or what, under the law can be an inventor? Can a machine be an inventor? More specifically, can artificial intelligence software be listed as an inventor on a patent application? This is the question that was recently addressed by the United States Court of Appeals for the Federal Circuit on Aug. 5.

In Thaler v. Vidal, the Appellate Court held that an inventor must be a naturalized person. Put another way, only human beings can be inventors. This case arose when Thaler tried to acquire patents for inventions developed by his Creativity machine known as DABUS. The United States Patent and Trademark Office (USPTO) denied Thalers applications, claiming that there must be a human inventor. Similarly, patent courts in the European Union, the UK, and Australia, all ruled against Thaler. Only South Africa allowed for an artificial intelligence inventor and granted Thaler a patent.

Here, in the United States, Thaler appealed the USPTO decision to the US District Court before appealing to the Appellate court. Both the District Court and the Appellate Court made the same conclusion that non-human entities cannot be inventors. No other American courts have addressed this issue, and unless the United States Supreme Court has an opportunity to consider the issue (in Thalers case or in a future case), the Federal Circuit Court of Appeals is the final authority on patent matters.

In its analysis of the issue, the Court of Appeals declined to engage in an analysis of the nature of an invention or the rights that might be attributed to artificial intelligence. Instead, the court left these issues open in favor of the safer, if perhaps no less controversial, practice of statutory interpretation. The Patent Act states that an inventor is the individual, or, if a joint invention, the individuals collectively who invented or discovered the subject matter of the invention. Because the patent statute does not define individual, the appellate court instead relied upon a previous United States Supreme Court case, Mohamad v. Palestinian Authority, in which the Supreme Court held that the word ordinarily refers to a human entity. Thus, the Appellate Court ultimately held that the term individual in the Patent Act refers only to natural persons and that artificial intelligence does not count as an inventor on a patentable invention.

The Mohamad case dealt with the application of the word individual as it pertains to the Torture Victim Protection Act of 1991 (the VPA). It is also worth emphasizing that in Mohamad, the Supreme Court held only that the term individual ordinarily means [a] human being, a person, and that its holding with regard to the VPA does not mean that the word individual invariably means natural person. Furthermore, the Supreme Court opinion dealt with whether a corporate or governmental agency could be considered an individual, and did not address the applicability of the word to a singular, individual, artificial intelligence.

The Appellate Court buttressed its decision denying the title of inventor to artificial intelligence by noting that nothing in the law shows or implies that the legislature intended the word individual to mean anything other than a natural person. The Court pointed to the fact that the Patent Act uses pronouns such as himself or herself when referring to inventors, indicating that congress did not intend to allow non-human inventors. The act does not use itself, which is the term that the court reasons Congress would have used if it intended to permit non-human inventors.

However, these are not the only ways in which the legislature could have illustrated an intent that the term individual be interpreted broadly. Indeed, as Thaler argued before the court, limiting innovation to natural persons is contrary to the general policy behind the Patent Act, namely to encourage innovation and public disclosure. As already stated, artificial intelligence could facilitate innovation at a rate and efficiency previously unseen. By limiting patent protections to inventions created purely by a human mind, the Appellate Court removes much of the incentive to utilize what promises to be the most powerful innovative tool in our toolbox. However, the court rejected this argument, and briefly categorized it as speculative, before referring again to its textualist approach.

Because the court relied on this textualist approach and did not consider the nature of inventorship, several questions remain to be answered. For example, because Thaler actually listed DARBUS as the inventor, Thaler presented no fact question regarding inventorship; he was simply asking the Court to determine that DARBUS could be an inventor. The Court expressly acknowledges this point: We are not confronted today with the question of whether inventions made by human beings with the assistance of AI are eligible for patent protection. So where, exactly, does the involvement of artificial intelligence in the inventorship process cross the line into an inventive activity that deprives the invention of patentability? How will companies navigate that line and structure their R&D to optimize the benefits of massive computing power and the potential for patent protection?

Additionally, Patents can only be granted if the invention is new and non-obvious. With the advent of powerful computers that can anticipate many inventions of which a human is capable, will those innovations, when eventually created by a human being, be determined to lack novelty and non-obviousness on the grounds that artificial intelligence as already thought of it? Will we reach a point in which artificial intelligence preempts the ability of a natural person to acquire a patent when that person eventually comes up with the invention on his own? And if that is the case, what effect will that have on the ultimate incentives for innovation generally?

Thaler plans to appeal to the US Supreme Court and argues that the Federal Circuit adopted a narrow and textualist approach that ignores the purpose of the Patent Act with real negative social consequences. Apart from any possible future action by the Supreme Court, further legislation is always possible after lawmakers, policymakers, think tanks, and academics have had the opportunity to re-evaluate existing law and its impact on innovation in light of growing experience with AI and emerging technologies. The Department of Commerce, which houses the USPTO, will no doubt continue to monitor this issue very closely and issue periodic reports. For further exploration of issues related to inventorship as related to artificial intelligence, see the USPTOs report here; and see generally, the USPTOs AI website.

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Artificial Intelligence and Inventorship: Federal Court of Appeals Determines That Patent Inventors Must Be Human - JD Supra

Artificial intelligence in China: Orchestras but no ultrasounds SupChina Skip to the content

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Artificial intelligence is growing rapidly in China, but there are still wide gaps in industrial application - SupChina

Artificial Intelligence (AI) is a critical technology for giving maritime companies a performance edge, but how can it be used to get ahead of the market? And how can AI accelerate digital transformation and meet the challenges of the upcoming energy transition?

Lloyds Registers new report, Artificial Intelligence in Maritime a learning curve, explores the current state of AI in the maritime industry, including market sizing and use cases, and explains how AI has the potential to revolutionise maritime operations and create significant competitive advantages for those companies that embrace it.

Written by maritime innovation consultancy Thetius, the report looks at how integration of AI in autonomous shipping, safety and navigational support systems, and vessel optimisation solutions will deliver immense value to users when implemented properly and efficiently.

Read more here:

Artificial Intelligence in Maritime - a learning curve helping you get the competitive edge. - All About Shipping - All About Shipping -

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Many futurists insist that technological advances will enable humans to upload our minds into computer systems, thereby allowing us to live forever, defying our biological limitations. This concept is deeply flawed but has gained popular attention in recent years. So much so, Amazon has a TV series based on the premise called Upload, not to mention countless other pop-culture references.

As background, the concept of mind uploading is rooted in the very reasonable premise that the human brain, like any system that obeys the laws of physics, can be modeled in software if sufficient computing power is devoted to the problem. To be clear, mind-uploading is not about modeling human brains in the abstract, but modeling specific people, their unique minds represented in such detail that every neuron is accurately simulated, including the massive tangle of connections among them.

Of course, this is an extremely challenging task. There are more than 85 billion neurons in your brain, each with thousands of links to other neurons.Thats around 100 trillion connections a thousand times more than the number of stars in the Milky Way. Its those trillions of connections that make you who you are your personality and memories, your fears and skills and ambitions.To reproduce your mind in software (sometimes called an infomorph), a computer system would need to precisely simulate the vast majority of those connections down to their most subtle interactions.

That level of modeling will not be done by hand. Futurists who believe in mind uploading often envision an automated process using some kind of super-charged MRI machine, that captures the biology down to the molecular level.They further envision the use of artificial intelligence (AI) software to turn that detailed scan into a simulation of each unique neuron and its thousands of connections to other neurons.

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MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

This is a wildly challenging task but is theoretically feasible. It is also theoretically feasible that large numbers of simulated minds could coexist inside a rich simulation of physical reality.Still, the notion that mind uploading will enable any biological human to extend their life is deeply flawed.

The real issue is that the key words in that prior sentence are their life.While it is theoretically possible with sufficient technological advances to copy and reproduce the form and function of a unique human brain within a computer simulation, that human who was copied would still exist in their biological body. Their brain would still be safely housed inside their skull.

The person that would exist in the computer would be a copy.

In other words, if you signed yourself up for mind uploading, you would not feel like you suddenly transported yourself into a computer simulation.In fact, you would not feel anything at all. The brain copying process could have happened without your knowledge while you were asleep or sedated, and you wouldnt have the slightest inkling that a reproduction of your mind existed in a simulation.

We can think of the copy as a digital clone or twin, but it would not be you.It would be a mental copy of you, including all of your memories up to the moment your brain was scanned.But from that time on, the copy would generate its own memories inside whatever simulated world it was installed in. It might interact with other simulated people, learning new things and having new experiences.Or maybe it would interact with the physical world through robotic interfaces.At the same time, the biological you would be generating new memories and skills and knowledge.

In other words, your biological mind and your digital copy would immediately begin to diverge. They would be identical for one instant and then grow apart. Your skills and abilities would diverge.Your knowledge and understanding would diverge. Your personality and objectives would diverge.After a few years, there would be significant differences. And yet, both versions would feel like the real you.

This is a critical point the copy would have the same feelings of individuality that you have. It would feel just as entitled to own its own property and earn its own wages and make its own decisions.In fact, you and the copy would likely have a dispute as to who gets to use your name, as you would both feel like you had used it your entire life.

If I made a copy of myself, it would wake up in a simulated reality and fully believe it was the real Louis Barry Rosenberg, a lifelong technologist. If it were able to interact with the physical world through robotic means, the copy would feel like it had every right to live in my house and drive my car and go to my job.After all, the copy would remember buying that house and getting that job and doing everything else that I can remember doing.

In other words, creating a digital copy through mind uploading has nothing to do with allowing you to live forever. Instead, it would create a competitor who has identical skills, capabilities, and memories and who feels equally justified to be the owner of your identity.

And yes, the copy would feel equally married to your spouse and parent to your children. In fact, if this technology was possible, we could imagine the digital copy suing you for joint custody of your kids, or at least visitation rights.

To address the paradox of creating a copy of an individual rather than enabling digital immortality, some futurists suggest an alternate approach. Instead of scanning and uploading a mind to a computer, they hypothesize the possibility of gradually transforming a persons brain, neuron by neuron, to a non-biological substrate. This is often referred to as cyborging rather than uploading and is an even more challenging technical task than scanning and simulating. In addition, its unclear if gradual replacement actually solves the identity problem, so Id call this direction uncertain at best.

All this said, mind uploading is not the clear path to immortality that is represented in popular culture. Most likely, its a path for creating a duplicate that would react exactly the way you would if you woke up one day and were told Sorry, I know you remember getting married and having kids and a career, but your spouse isnt really your spouse and your kids arent really your kids and your job isnt really...

Is that something anyone would want to subject a copy of yourself to?

Personally, I see this as deeply unethical. So unethical, I wrote a cautionary graphic novel over a decade ago called UPGRADE that explores the dangers of mind uploading. The book takes place in a future world where everyone spends the majority of their lives in the metaverse.

What the inhabitants of this world dont realize is that their lives in the metaverse are continuously profiled by an AI system that observes all their actions and reactions, so it can build a digital model of their minds from a behavioral perspective (no scanning required). When the profiles are complete, the fictional AI convinces people to upgrade themselves by ending their life and allowing their digital copies to fully replace them.

When I wrote that book 14 years ago, it was intended as irony. And yet theres an emerging field today that is headed in this very direction. Euphemistically called the digital afterlife industry, there are many startups pushing to digitize loved ones so that family members can interact with them after their death. There are even startups that want to profile your actions in the metaverse so you too can live forever in their digital world. Even Amazon recently stepped into this space by demonstrating how Alexa can clone the voice of your dead grandmother and allow you to hear her speak.

With so much activity in this space, how long before a startup begins touting the cost-saving benefits of ending your life early and allowing your digital replacement to live on? I fear its just a matter of time.My only hope is that entrepreneurs will be honest with the public about the reality of mind uploading its not a pathway to immortality.

At least, not the way many people think.

Louis Rosenberg, Ph.D., is a pioneer in fields of VR, AR and AI. He earned his Ph.D. from Stanford University, has been awarded over 300 patents, and founded a number of successful companies. Rosenberg began his work at Air Force Research Laboratorywhere he developed the first functional augmented reality system to merge real and virtual worlds. Rosenberg is currently CEO of Unanimous AI, the chief scientist of the Responsible Metaverse Alliance and global technology advisor to the XR Safety Initiative (XRSI).

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Molecular findings

The variants occurred de novo in 12 individuals, were maternally inherited in Individuals K and L, and paternally inherited in individual O. One parent of affected Individual T, Individual U, showed a low level of mosaicism for the variant (with only 2 out of 298 sequencing reads for this variant found in her blood). Nine individuals had unknown modes of inheritance. A majority, 20, are truncating variants (frameshift or nonsense), and five are missense (with three of five belonging to the same family). Twenty-one distinct variants were identified (Table1), with locations shown in Fig.2 [18].

The coding exons for ANKRD11 are depicted to scale. Abbreviations: aa amino acid. The figure was made using: https://www.cbioportal.org/mutation_mapper.

Truncating variants are classified by ACMG criteria [19] as: PVS1 null variant (nonsense, frameshift) in a gene where loss of function is a known mechanism of disease. Some variants are classified as PS2 De novo (both maternity and paternity confirmed) in a patient with the disease and no family history. One missense variants in our cohort (p. (Val586Met) was seen in a heterozygous control individual in the Genome Aggregation Database (GnomAD), thus calling into question its pathogenicity. It is also formally possible that the one individual in GnomAD might be mildly affected. The mother with this variant (individual M) has a very mild phenotype whereas her children (individuals K and L) have phenotypes more consistent with KBG syndrome. However, a recent preprint [20] demonstrated that some missense variants do impair ANKRD11 ability and/or stability, but that these variants mainly localize in the Repression Domain 2. Those authors also tested one variant in the Repression domain 1 (p.Leu509Pro), which turned out to have no effect on ANKRD11 stability or activity. The p.(Val586Met) variant of individuals K, L, and M also falls within the Repression Domain 1, and it has a borderline CADD score (23.9) and is not as highly conserved as the other missense variants. In addition, the affected nucleotides and corresponding amino acid are also not highly conserved when the sequence is aligned with other species. Per DeepGestalt, these individuals (K, L, M) did not have KBG syndrome listed in their top 30 differentials. Segregation analysis with the mother and sister of Individual M is not yet available. While the mother has very mild clinical features of KBG syndrome, the sister (aunt of Individuals K and L) is potentially reporting more severe symptoms. Ultimately, the pathogenicity of the variant (p.(Val586Met)) is still uncertain.

A different missense variant (p. Arg2536Gln) arose de novo and was initially classified as a variant of uncertain significance because it had not been previously reported. However, it has been reclassified because of new information available: two additional patients carrying the variant. One is reported in Clinvar (https://www.ncbi.nlm.nih.gov/clinvar/variation/1012410/?new_evidence=false), a patient in whom the variant was maternally inherited (referred to as Individual Z in Supplementary Information), but who was unavailable for videoconferencing. In the other previously reported patient, the variant has arisen de novo and was classified as pathogenic [21]. Although a more extensive cosegregation of the patient reported in Clinvar is not available, since phenotypes characteristic of KBG syndrome are seen in three individuals possessing this variant, the variant is reclassified to likely pathogenic. Further details about these cases can be found in Supplemental Text and Case Summaries.

As of April 2022, there are 429 putative missense or non-frameshift deletion, substitution or insertion variants in ANKRD11 submitted to ClinVar [22], with many of these listed as variants of uncertain significance (Supplementary Table2), with bioinformatic analyses providing a suggested consensus classification for each variant.

Median age of the 25 individuals was 11 years and average age was 15 years (range=159). One comes from a consanguineous family, roughly half (n=12) had a history of congenital abnormalities in the family, and eight had relatives with intellectual disabilities.

The parents of individuals B, D, T, and Y had histories of miscarriage. The variant was de novo for individual B, whereas the parent of individual T (Individual U) was mosaic for the missense variant (as noted above). The mother of individual Z has a history of several miscarriages early in pregnancy around six weeks of age. The inheritance pattern is unknown for individuals D and Y.

The parents in this study (M, P, U) generally had mild phenotypic features. Individual M, the mother of K and L, possessed some distinct facial traits (e.g., thick eyebrows, anteverted nares, broad nasal base), however, the overall constellation of features was not typical of KBG syndrome. She did not present with common features such as developmental delay, macrodontia, or short stature. Conversely, individual P, the father of O, presented with global developmental delay, macrodontia, and short stature among other common traits of KBG syndrome. Lastly, individual U, the mother of T, had mild facial features (e.g., synophrys, thick eyebrow, wide nasal bridge, prominent nasal tip) with speech delays and seizures in childhood.

The overall frequency of certain phenotypic features is shown in Table2, and these are reviewed in further detail in the following sections.

Height at the time of videoconference clustered into 398th centile (44%), below 3rd centile (24%) and above 98th centile (12%) with a median height of 140.0 29.4cm. Weights at time of videoconference clustered into 3-98th centile (48%), below the 3rd centile (20%), and above 98th centile (4%), with a median weight of 27.8 29.1kg. Of the three individuals who had heights above the 98th centile at time of videoconference, one had been put on growth hormone for approximately 24 years (Individual J) (Table3). Birth length clustered into 398th centile (44%), above 98th centile (8%), and below 3rd centile (8%), with a median length of 49.0 6.3cm. Birth weight clustered between 398th centile (64%), and below the 3rd centile (16%) with a median birth weight of 3 0.7kg.

The photographs with permission for publication are shown in Fig.3. At least one distinctive facial feature common to KBG patients was present in every individual interviewed. Defining facial characteristics include thick eyebrows with synophrys, prominent eyelashes, wide nose, thin upper lip vermillion, and macrodontia. Many have a triangular face or pointed chin and a broad or prominent forehead.

Characteristic features include bushy eyebrows (A, C, D, E, I, K, M, O, P, R, T, U, V, Y), long eyelashes (C, D, I, L, O, P, S, X,), triangular face (A, G, K, R, V) and most had a wide nasal bridge or tip and a thin upper vermillion.

Pairwise ranks of the 25 photos in Fig.4 suggest most patients described in this analysis share similar facial phenotypes. In a gallery of 3533 images with 816 different disorders and 25 KBG patients, 15 out of 25 KBG patients had at least one other KBG patient in their top-10 rank, and 21 out of 25 patients had at least one other patient in their top-30 rank. Other than U being an outlier, there was a cluster containing the set of patients with three sub-clusters (P, J, F, and M), (O, H, R, Y, V, G, and I), and (Q, S, D, and E). Patient U was an outlier, perhaps due to the low-level mosaicism for this variant. No clear clusters were seen when segregated by type of genetic variant (missense, frameshift, nonsense). The similarity between family members is a known confounder in the analysis of syndromic similarity. On average, family members with the same disorder are closer in the clinical face phenotype space than unrelated individuals with the same disorder. That said, in one family, we do not see an increased similarity between M, K, and L.

Sub-cluster P, J, F, M present with synophrys and wide noses. Sub-cluster O, H, R, Y, V, G, I present with thick eyebrows, prominent/broad nasal tips, macrodontia, triangular faces and pointed chins. Sub-cluster Q, S, D, E present with anteverted nares, broad nasal tips, and macrodontia. Link: https://db.gestaltmatcher.org/; individual links to each patient in Supplemental Text. Note: Individual E did not consent to having their photo published, however, a frontal photo was input into the GestaltMatcher and DeepGestalt algorithms.

KBG syndrome was recommended among the top 30 syndromes and ranked as the first (i.e., most likely) diagnosis for 28% (n=7) of individuals, second for 40% (n=10), and third or fourth for 12% (n=3). Overall, 80% (n=20) of patients photos analyzed had KBG syndrome ranked in their top-five potential diagnoses out of the 30 possible suggested syndromes from among the 300+ syndromes currently recognized by the DeepGestalt algorithm. Among the 20 with KBG in the top-five rank, seven had a high gestalt score, 10 had medium gestalt, and three had low gestalt. Fourteen had a medium feature score, five had a low score, and one was unranked for features of KBG (see Supplementary Table3). Individuals B, F, and J initially submitted photos where they were wearing glasses. After analyzing photos without glasses, the ranking of KBG surprisingly dropped from two to six for individual B and from two to three for individual J. Ranking did not change for individual F. While KBG ranking fluctuated, the gestalt and feature levels did not change between the photos with and without glasses for any of the three individuals.

Five individuals (K, L, M, P, U) did not have KBG syndrome appear as a differential diagnosis out of 30. First ranked diagnoses instead included Cornelia de Lange, Williams-Beuren, Rubinstein-Taybi, Angelman, and mucopolysaccharidosis. Notably, Individual P was 5560 years old at the time of the videoconference whereas Individual U was 3035 years old, and both of them initially submitted pictures of themselves around those ages. These ages fall above our median age of 11 years and the age at which most individuals are diagnosed with KBG syndrome. DeepGestalt relies on the photos that it is trained on, so older age photos may not perform as well. Additionally, individual U has very low-level mosaicism for this variant, potentially resulting in lower phenotypic expression of facial features. The other three individuals who were unranked (K, L, and M) are all from the same family and possess the same missense variant (Table1) with questionable pathogenicity.

With PEDIA score, the disease-causing gene ANKRD11 is ranked at the first place in 18 out of 25 (top-1 accuracy: 72%). When looking at the top-10 genes, ANKRD11 is listed in the top-10 genes in 22 out of 25 (top-10 accuracy: 88%). All have ANKRD11 in their top-30 genes.

Eight reported an intelligence quotient (IQ) score, with a mean of 734.84 (range=6480) as measured by the Weschler Intelligence Scale (3rd to 5th edition). A majority, 68% are considered mildly to moderately intellectually disabled based on level of functioning. Global developmental delays prior to 5 years were seen in 68% (n=17), with nine being classified as mild. Median age of crawling onset was 12 months (range=924) (n=8), walking onset 22 months (range=12.536) (n=10), and speech onset 30 months (range=1936) (n=6). Selective mutism and absent speech were observed in three individuals.

Common types of seizures reported included myoclonic, tonic-clonic, and absence with no specific type predominating [23]. Electroencephalogram (EEG) abnormalities were documented in three of 11 individuals with seizures. According to maternal report, Individual E was meeting speech and motor milestones until the onset of myoclonic seizures, complex partial seizures, and verbal tonic seizures with respiratory distress around 0.52 years of age. Similarly, individuals H, K, R, S, T, U, X, and Y reported histories of various types of seizures and concurrent speech and motor delays. Brain abnormalities detected on magnetic resonance imaging (MRI) included pineal cyst, arachnoid cyst, choroid plexus cyst, subdural hemorrhage, and small pituitary gland.

Abnormal mood included abnormal emotion or affect, depression, and/or anxiety, self-injurious behavior including self-biting. Individuals E, O, Q, and R report absent or high pain threshold. O has a history of a fractured foot and a dislocated kneecap with bone scans showing normal density. Impaired tactile sensation was reported in two individuals (M,S).

Six had chronic otitis media, with five of six having concurrent hearing impairment. Those experiencing chronic otitis media likewise had a preauricular pit, abnormal or blocked Eustachian tubes, abnormality of the tympanic membrane, enlarged vestibular aqueduct, choanal atresia, and increased size of nasopharyngeal adenoids. Hearing loss and recurrent infections including sinus, chronic ear, and upper respiratory infections were present in four individuals (O, P, Q, Y). Of the six with palatal anomalies, four had difficulties feeding.

Of note, individual A was diagnosed with osteopenia, and later osteoporosis, at 1520 years with low bone mineral densitometry in the lumbar spine, hip, and femoral neck. An x-ray of his left hand and wrist was performed which revealed physeal closure of the bones, excluding delayed bone maturation. Individual S has visible sacral dimple and was referred to neurology for gait disturbance and urinary incontinency. MRI of her lumbar spine revealed a tethered spinal cord.

Cardiac abnormalities were seen in approximately half the participants and while many resolved without the need for surgical intervention, individual K had Tetralogy of Fallot with pulmonary valve-sparing surgical repair at ~36 months of age. Individual T had mitral valve repair at around one year of age.

Participants F, M, S, T, U had presumed diagnoses of abdominal migraines, characterized by stomach pain, nausea, and vomiting. In F, the abdominal migraines were accompanied by cyclic vomiting syndrome. Reports described her episode as significant pain causing writhing with soft, nontender abdomen normal bowel sounds on examination.

Short stature is a common phenotype in those with KBG syndrome with up to 66% below the 10th centile in height [5]. Individuals H, J, and O were administered growth hormone. J was born with a length below 1st centile and weight at 57th centile. After receiving somatropin injections from 3.5 years to 5.7 years of age, his height is at the 13th centile and weight is at 24th centile. O was given growth hormone from approximately 6 years to 11 with positive improvement in weight (11th percentile at birth and is now at 45th percentile). Efficacy of hormone supplementation is unknown for H. Reports of precocious puberty, immunodeficiency, recurring infections, allergies are also common.

Urogenital disorders were seen in 48% (n=12) of individuals, with seven being female and five being male. Of note, four males were diagnosed with cryptorchidism. Other diagnoses included abnormalities of the urethra and/or bladder, recurrent urinary tract infection, pollakiuria, polyuria, and enuresis.

A majority (56%) reported abnormalities of skin, nails, and hair, which included: hirsutism, low anterior hairline or abnormal hair whorl, cellulitis, keratosis pilaris, acne and dry skin, psoriasiform dermatitis, eczema, fingernail dysplasia, and recurrent fungal infections.

Original post:

KBG syndrome: videoconferencing and use of artificial intelligence driven facial phenotyping in 25 new patients | European Journal of Human Genetics -...