Researchers at Massachusetts General Hospital and Harvard Medical School have developed machine learning-based methods to predict which patients with early-stage melanoma have the highest risk for disease recurrence. The methods could help reveal those who may benefit from greater surveillance or adjuvant immunotherapy, the investigators wrote in a study published in npj Precision Oncology. "In 2021, immune checkpoint inhibitors that historically have been used to treat more advanced melanomas received approval for the management of stage IIB and IIC disease," Yevgeniy R. Semenov, MD, MA, assistant professor and principal investigator of the Dermatology Clinical Informatics Laboratory at Massachusetts General Hospital, told Healio. "Clearly, not everyone who has early-stage disease will experience a recurrence. If we look at historical data, only about 20% to 30% of even the stage IIB and IIC groupings would experience recurrence, but that would mean giving these immunotherapies to 100% of patients in a situation where maybe only 20% to 30% would benefit." Semenov and colleagues amassed 1,720 early-stage melanomas and extracted 36 clinical and pathologic features of these cancers from electronic health records to predict patients' recurrence risk with machine learning algorithms.

ATLANTA -- An ultrasound-based artificial intelligence classifier of thyroid nodules identified benign nodules with sensitivity similar to fine-needle aspiration, according to data presented at ENDO 2022. "Artificial analysis of thyroid ultrasound images can identify nodules that are very unlikely to be malignant," Nikita Pozdeyev, MD, PhD, assistant professor at University of Colorado Anschutz Medical Campus, told Healio. "These are mostly spongiform nodules that have a less than 3% probability of malignancy." Pozdeyev and colleagues trained a supervised deep learning classifier of thyroid nodules on 32,545 images of 621 thyroid nodules acquired from University of Washington. The classifier was then tested on an independent set of 145 nodules collected from the University of Colorado.

Artificial intelligence can alleviate administrative burdens, improve diagnostic accuracy, identify patients most at risk for certain diseases and reduce unnecessary procedures, according to a recent paper. Yet, "most primary care providers do not know what it is, how it will impact them and their patients and what its key limitations and ethical pitfalls are," Steven Lin, MD, the author of the paper and family medicine service chief and head of technology innovation in the division of primary care and population health at Stanford Medicine, wrote in the Journal of the American Board of Family Medicine. He added that primary care is the ideal medical specialty to take charge in what he called the "health care artificial intelligence (AI) revolution." Lin shared more details on this emerging technology and how primary care can maximize its potential in an interview with Healio. Healio: Why should primary care lead the "health care AI revolution"?

A score derived from machine learning that included information from stress cardiac magnetic resonance effectively predicted 10-year all-cause …

A deep learning score accurately predicted PD-L1 expression among a cohort of patients with non-small cell lung cancer who underwent PET/CT scans, according to study findings published in Journal for ImmunoTherapy of Cancer. "This study is important, as it is the single largest multi-institutional radiomic study population of [patients with NSCLC] to date treated with immunotherapy who had PET/CT scans that were used to predict PD-L1 status and subsequent treatment response," Robert J. Gillies, PhD, chair of cancer physiology and vice chair of radiology research at Moffitt Cancer Center, said in a press release. "Because images are routinely obtained and are not subject to sampling bias per se, we propose that the individualized risk assessment information provided by these analyses may be useful as a future clinical decision support tool pending larger prospective trials." Gillies and colleagues developed a deep learning score to predict PD-L1 expression, durable clinical benefit, PFS and OS among 697 patients with NSCLC treated with immune checkpoint inhibitors across three institutions. According to study results, the score enabled researchers to distinguish between patients with PD-L1-positive and PD-L1-negative status.

"A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses," Rebecca Ihrie, PhD, and Jonathan Irish, PhD, associate professors in the department of cell and developmental biology at Vanderbilt University, and colleagues wrote. "We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival." Ihrie and Irish told Healio what prompted this research, implications of the findings and what future research should entail. Question: What prompted this research? Ihrie: Cancers are now being studied using single-cell approaches, through which we can learn about the presence and abundance of different subsets of cells within the sample.

Deep neural networks identified potential adverse outcomes and atrial fibrillation from 12-lead ECGs that were originally interpreted as normal, according to new research presented at the American Heart Association Scientific Sessions. "Applications of machine learning and artificial intelligence techniques to problems in health care are increasingly common, but generally focus on diagnostic problems such as detecting features in an image of classifying a current diagnosis based on present features," Christopher M. Haggerty, PhD, assistant professor in the department of imaging science and innovation, and Brandon K. Fornwalt, MD, PhD, associate professor and director of the department of imaging science and innovation, both at Geisinger in Danville, Pennsylvania, told Healio. "Few studies have been able to apply machine learning to the task of predicting future events or patient outcomes. This work is among the first to demonstrate proof of concept for predicting a future patient event -- 1-year mortality -- with good performance based solely on 12-lead electrocardiography data." Sushravya M. Raghunath, PhD, math and computational scientist in the department of imaging science and innovation at Geisinger, and colleagues analyzed 1,775,926 12-lead resting ECGs of 397,840 patients from 34 years of archived medical records.

Developer Kevin Kim lived through Hurricane Sandy in 2012 and saw the deep impact the storm had on close friends -- not only on physical belongings and health, but on their mental well-being. One of Kim's friends, for example, had a tree crash through his roof, and though no one was physically hurt, dealing with the insurance and finances after the storm took a heavy toll on his friend. The experience spurred Kim and his teammates -- Christopher McKinney, Sunjae Shim, Tony Park, and Xuelong Mu -- to join forces and create Healios, an online platform that uses a conversational interface and AI to help connect those who need mental healthcare with the right case worker. The team's solution has been named a top-five finalist in the Call for Code 2019 Global Challenge. "There are a lot of stressors that are being constantly inundated with needs that pop out of nowhere, that they never had to deal with," Kim said.

AsTeR (Europe) – During natural disasters, emergency call centers are overwhelmed and lack the human resources to deal with the sudden uptick in calls. Project AsTeR helps prioritize these calls based on their level of emergency. Instead of being directly connected to an operator, victims are asked to briefly explain their emergency over the phone. Their responses are then converted to text and analyzed to extract key information, such as the number of victims, type of emergency and location. AsTeR then provides first responders with a map identifying areas with high levels of emergency based on the number of people involved and the type of injuries.

A novel imaging approach using artificial intelligence was associated with improved detection of parameters associated with melanoma, according to results presented at the International Conference on Image Analysis and Recognition. The researchers suggested that a number of quantitative imaging approaches to dealing with melanoma have focused largely on skin lesions using "hand crafted imaging features." The current study employed "machine-learning" software, which records abstract quantitative features on images and can model physiological traits of the patients, according to study background. The two features of melanoma that were assessed in the analysis were eumelanin and hemoglobin concentrations observed on dermal imaging. The researchers created a non-linear random forest regression model culled from the images.