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Tag: Screening

MHT Delivers New Technology for Measuring and Improving Mental Wellness

Article Excerpt: Mental Health Technologies (MHT) offers a rapidly growing cloud-based platform primary care physicians and mental health professionals use to screen and test for mental health disorders, including depression and substance abuse. MHT helps providers identify areas where their patients are struggling and refers them to the proper behavioral healthcare professional…SmarTest is a tool that uses intelligence and historical data to define when-and how-a patient should be tested for various mental health conditions. It can base its decisions on patient information, such as age, gender, or other demographics.

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Article Source: Accesswire


Artificial Intelligence Tools Quickly Detect Signs of Injection Drug Use in Patients’ Health Records

Article Excerpt: An automated process that combines natural language processing and machine learning identified people who inject drugs (PWID) in electronic health records more quickly and accurately than current methods that rely on manual record reviews. Currently, people who inject drugs are identified through International Classification of Diseases (ICD) codes that are specified in patients’ electronic health records by the health care providers or extracted from those notes by trained human coders who review them for billing purposes. But there is no specific ICD code for injection drug use, so providers and coders must rely on a combination of non-specific codes as proxies to identify PWIDs—a slow approach that can lead to inaccuracies.

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Article Source: Medical XPress


Freakin’ Cool Tech: Rytek

Article Excerpt: RyTek Medical of Lebanon continues to find new ways to improve biomedical devices, having already found success in the areas of traumatic brain injury monitoring, early stroke detection, cancer sensing and imaging, and now dental surgery guidance through the use of bioimpedance-based medical technologies… Ryan Halter, founder and CEO of RyTek, and an associate professor of engineering at Dartmouth College, says he was approached by a dental surgeon who wanted Halter and his academic lab to tackle the challenge of providing feedback during surgery.

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Article Source: Business NH


Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): A retrospective deep learning study

Afshar M, Sharma B, Dligach D, Oguss M, Brown R, Chhabra N, Thompson HM, Markossian T, Joyce C, Churpek MM, & Karnik NS (2022). Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. The Lancet (British Edition), 4(6), e426–e435.

SMART-AI is a substance misuse algorithm to support referral to treatment using artificial intelligence. The tool is a machine learning classifier tool for identifying alcohol misuse, opioid misuse, and non-opioid drug misuse using clinical notes collected in the electronic health records. Using of patients (N=16,917) during the first 24 hours of hospitalization, the prospective primary analysis consisted of temporal validation done to examine misuse classification and the association to outcomes and treatment referrals. Results from manual screening identified 3.5% of patients had any type of substance misuse and 11% of these patients had more than one type of misuse. SMART-AI showed good calibration and validity, with a false negative rate of 0.18-0.19 and a false positive rate of 0.03 between non-Hispanic Black and non-Hispanic White subgroups. The results also show prediction performance can change over time or in differing patient settings, where prevalence of substance misuse varies. There were also significant changes during the COVID-19 pandemic which required the algorithm to be recalibrated. Overall, this study demonstrated that clinical notes from the electronic health record during initial hospitalization can be used to identify substance misuse accurately with the help of artificial intelligence and may be used to potentially improve screening rates.


Artificial Intelligence May Help to Classify Colorectal Polyps

Article Excerpt: Researchers at the Geisel School of Medicine at Dartmouth College in Hanover, New Hampshire, recruited 15 pathologists to analyze and classify specimens of the four most common types of colorectal polyps from 100 slides. In one session, about half the doctors used the AI-augmented digital system while the others used just a microscope. After a 12-week break, each group switched techniques and read the same slides again. The pathologists accurately classified the samples 73.9% of the time when using just a microscope and 80.8% when they used the AI-augmented digital system… “This is about helping pathologists make this subtype classification more accurately and more efficiently,” says Saeed Hassanpour, an associate professor at the Geisel School of Medicine who was the senior author of the study.

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Article Source: Cancer Today Magazine


Doctors Using AI, Supercomputer To Predict And Prevent 50% Of Mental Illness

Article Excerpt: Almost a billion people globally suffer from some form of mental illness. But doctors from Cincinnati Children’s Hospital are using artificial intelligence and the world’s second most powerful supercomputer for early diagnosis. And that, they say, can make all the difference. “If we can identify this early … we can treat for and alleviate almost 50% of the mental illness that goes into adulthood,” Dr. John Pestian told me recently on the TechFirst podcast. “So catching it young, catching it early, and giving care is a very important part.”

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Article Source: Forbes


New Predictive Computer Program Could Help Detect Individuals at High Risk of Depression

Article Excerpt: A team of scientists from Nanyang Technological University, Singapore (NTU Singapore) has developed a predictive computer program that could be used to detect individuals who are at increased risk of depression. In trials using data from groups of depressed and healthy participants, the program achieved an accuracy of 80 per cent in detecting those individuals with a high risk of depression and those with no risk.

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Article Source: News Medical


Smartwatch to Detect Opioid Cravings, Offer Interventions

Article Excerpt: In what could prevent opioid users from misusing drugs, experts are working on a project to develop smartwatches with the ability to detect emotional and psychological patterns opioid users show hours before indulging in substance abuse. The project is funded by the National Science Foundation’s Smart and Connected Health program, an independent agency funded by the United States government. The University of Massachusetts recently announced that “a research team … has received a $1.1 million grant to further develop a smartwatch sensor designed to support the long-term recovery of people with opioid use disorder (OUD).”

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Article Source: TRT World


New AI Model Accurately Classifies Colorectal Polyps Using Slides from 24 Institutions

Article Excerpt: An artificial intelligence (AI) model for automated classification of colorectal polyps could benefit cancer screening programs by improving efficiency, reproducibility, and accuracy, as well as reducing access barriers to pathological services. In a new study out of Dartmouth’s and Dartmouth-Hitchcock’s Norris Cotton Cancer Center, a computer science and clinical research team led by Saeed Hassanpour, PhD, trained a deep neural network to do just that. Not only can their model distinguish the four major types of colorectal polyps at the level of practicing pathologists, as evaluated on a dataset across multiple external institutions, but also proves that a model designed using data from a single institution can achieve high accuracy on outside data.

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Article Source: Dartmouth Cancer Center News