CHARLOTTESVILLE, Va. (WVIR) – University of Virginia doctors are giving us a glimpse into what the future holds for artificial intelligence as it relates to pathology.

“Currently the systems that are being developed are in research labs. There are actually two areas of medicine that are going to be primarily impacted by artificial intelligence related to image interpretation and that’s radiology and pathology,” James Harrison, an associate professor of pathology at UVA, said.

For the past two years, Harrison along with others in the College of American Pathologists’ Machine Learning Workgroup have been looking into the potentials of artificial intelligence and machine-learning. He says radiology is a little ahead of pathology artificial intelligence systems.

“There are systems that are actually in use now for finding problem areas in mammograms for examples, interpreting CT scans, finding evidence of stroke,” Harrison said. “We’re going to see similar kinds of systems become available for pathology.”

Harrison says these systems for pathology have not been approved for sale yet because they’re still being developed. However, he expects to start to see them over the next several years.

“The kind of artificial intelligence that’s causing most interest now is machine-learning and that looks for patterns in the data and learns those patterns on its own and then uses those patterns from existing data to make interpretations of patterns present in new data that it’s shown,” Harrison said. “The machine-learning systems we have now can perform a task based on lots of different input data so you can take a whole image and detect patterns in that, whereas previously, we might do something similar but only with a few data elements at a time. They’re particularly good at taking large amounts of input and responding to that.”

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Doctors have developed a new data mining method to detect many young people with emerging psychosis.

The new methods, based on advanced data mining to pick up early risk sign from schools, hospitals, and general doctors, will be presented at the ECNP virtual congress, and is in press with a peer-reviewed journal.

Psychosis is a condition which causes you to lose touch with reality, causing you to suffer from hallucinations or delusions.

There are a variety of possible causes, including migration and social stress, trauma, substance abuse, etc. It represents a significant care burden, affecting about 20 million people and costing Europe around €94 billion European every year (2011 estimate).

Clinical experience has shown that the best way to manage it is to stop it developing. Over the last 25 years doctors have developed ways of detecting young people at risk of developing psychosis and predicting which young people might go on to develop the disorder, and so have been able to take steps to lower risk.

However the way clinicians were detecting young people was not systematic and may have missed many at-risk people. Now doctors in the UK have developed new data mining methods which can potentially detect most people who are at risk of developing psychosis.

This, in turn would allow to offer them preventive psychological interventions that can halve their risk of developing full-blown psychosis.

“We have developed a data mining method (using Natural Language Processing), to search medical records for those at risk of progressing to psychosis. Many medical records are fairly unstructured, with information of mental health being hidden in sections which do not allow systematic research.

Our data-mining system does a more complete search of the records people who have been referred to hospital (secondary care), looking for keywords such as weight loss, insomnia, cocaine, guilt, etc. We can look for 14 different terms which we then evaluate for the risk of psychosis.

At that point patients might be invited for a one-to-one interview. We have found that prevention can halve the risk of psychosis developing”.

The systems have evaluated 92,151 patients over a long follow up period. They were able to confirm that their method worked well to detect young people at risk, although Professor Fusar-Poli cautioned that “these results need further replication in other countries before they can enter clinical routine but they look very promising.

Replication will be facilitated by international research consortia such as the ECNP-funded Prevention of Mental Disorders and Mental Health Promotion Network”

Prof. Fusar-Poli suggested that detection of these young people is the first step towards prevention. Preventive interventions in these people can translate in several benefits:

“This translates into real benefits. Although the initial cost for establishing specialised services detecting young people at risk of psychosis is greater, intervening before the onset of psychosis is associated with fewer treatments, fewer days in hospital, in addition to the tangible and social health benefits, meaning that the NHS saved around £1000 per patient diagnosed.

Our detection systems can extend these benefits to many other young people who might be at risk of psychosis”
Professor Fusar-Poli will present the work while chairing a session on the prevention of mental disorders (see below) at the ECNP congress.

“We have been working with the ECNP special group on Prevention of Mental Disorders and Mental Health Promotion, and with the EU-Funded European Brain Research Area  to set up a Europe-wide system of advance warning for young people at risk of psychosis. It is essential that we bring the best expertise to bear on this problem, and we can all learn from the experience of others”

Commenting, Professor Andreas Meyer-Lindenberg (Mannheim), member of the ECNP executive board said:
“This work is an excellent example of the transformative role of artificial intelligence and big data processing in psychiatry. While much attention in this field has been focused on biological data and biomarkers, this result shows the gains that can be made if the wealth of written information that clinicians produce in their daily work is mined using innovative approaches.”

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A team of researchers at the James A. Haley Veterans’ Hospital in Tampa, Florida, is revolutionizing the way cancer is documented by enlisting the help of a computer to diagnose the disease in one of the largest patient populations in the nation: veterans.

Sophisticated artificial intelligence is capable of drastically altering how cancer is diagnosed and treated by learning to distinguish imagery of tissue containing cancerous cells from pictures of healthy tissue, a recent study in the Federal Practitioner journal claims.

“Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis,” the study’s authors wrote.

To test machine learning software, researchers uploaded hundreds of microscopic images of commonly diagnosed forms of the disease, such as lung or colon cancer, along with pictures of non-cancerous cells. At the conclusion of the test, the software — both Google- and Apple-based versions were tested — not only distinguished cancerous cells from non-cancerous tissue with a success rate of better than 90 percent, but it also indicated the exact form of cancer it was analyzing.

The ability of user-friendly machine learning software to learn and efficiently perform traditional human tasks in less time will alleviate some of the demand on medical practitioners who are already being stretched thin, the authors claim.

By coupling AI with a growing list of telehealth options, specialists have the potential to reach patients from anywhere in the world. Greater accessibility would especially benefit the millions of patients in the VA’s healthcare system, many of whom live in remote, rural areas where specialists or facilities needed to treat unique diseases are scarce at best.

A collaborative doctor-AI system can also diminish patient wait times and effectively eliminate the time-consuming paperwork analysis that has always bogged down practitioners. What it won’t do, according to one of the study’s authors, is replace its Homo sapien counterparts.

“Our ultimate goal would be to create programs that can be rolled out in the entire VA system so that pathologists who are working solo, or maybe there are two pathologists in some small VAs, would have the benefit of having something that is helping them become more productive, help them prioritize the workload and improve quality,” Dr. Andrew Borkowski said in a VA release.

And while the hope of machine learning enthusiasts is to eventually apply AI-assisted healthcare on a global scale, early testing using the VA’s expansive patient base allows for the mining of data from a seemingly limitless source.

The myriad imagery generated from the nearly 50,000 cancer diagnoses of veterans each year, for example, will enable AI software to analyze more data, learn faster, and expand application to other demographics and diseases at a pace other healthcare systems cannot match.

All this is not to say there won’t be obstacles to overcome before AI can be considered entirely viable — ensuring the impeccable accuracy of its decision-making paramount among them.

In 2019, Google-run AI software was fed hundreds of images and tested to determine whether it could predict the early onset of a deadly kidney disease. Two of every three AI-generated results yielded false positives. Significant diagnostic errors like that can be detrimental to practitioners who then follow up on phantom diseases using valuable time that could be spent treating patients in dire need, Mildred Cho, associate director of the Stanford Center for Biomedical Ethics, told WUSF News.

Continued success in machine learning trials like the one at the James A. Haley Veterans’ Hospital, however, bode well for AI’s future implementation into healthcare. Researchers hope continuously evolving software, such as Apple-produced AI that is now capable of recognizing images that have been rotated, flipped, or cropped, will help alleviate a glaring industry-wide trend.

The “number of pathologists in the U.S. is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology,” the study’s authors wrote. “These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.”

Future application of machine learning AI, the study concluded, will be immeasurably beneficial in diagnosing and documenting everything from various forms of cancer to non-cancerous diseases, brain hemorrhages, blood disorders, infections, and inflammatory issues.

“The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.”

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