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AI Promising for Detecting Adenomas in Patients With Lynch Syndrome - Physician's Weekly


THURSDAY, Jan. 5, 2023 (HealthDay News) -- For patients with Lynch syndrome (LS), artificial intelligence (AI)-assisted colonoscopy is promising for detecting adenomas, especially flat adenomas, according to a study published online Dec. 26 in the United European Gastroenterology Journal. Robert Hüneburg, M.D., from the National Center for Hereditary Tumor Syndromes at University Hospital Bonn in Germany, and colleagues examined the diagnostic performance of AI-assisted colonoscopy compared with high-definition white-light endoscopy (HD-WLE) in adult patients with LS, with a pathogenic germline variant (MLH1, MHS2, MHS6) and at least one previous colonoscopy (interval, 10 to 36 months). A total of 96 patients were included in the analysis. The researchers found that adenomas were detected in 12 of 46 and 18 of 50 patients in the HD-WLE and AI arms, respectively (26.1 versus 36.0 percent). Detection of flat adenomas (Paris classification 0 to IIb) was increased significantly with use of AI-assisted colonoscopy (numbers of detected flat adenomas: 17 of 30 versus four of 20).

AI listens to toilet sounds to guess whether people have diarrhoea

New Scientist

An artificial intelligence can detect diarrhoea with up to 98 per cent accuracy by analysing the sounds emanating from toilets. This skill could help us track outbreaks of diseases such as cholera. Maia Gatlin at the Georgia Institute of Technology and her colleagues collected 350 recordings of toilet-based sounds from YouTube and sound database Soundsnap – covering standard defecation, diarrhoea, urination and flatulence. The researchers then used 70 per cent of the recordings to train an AI to recognise audible differences between the four types of excretion. Once they confirmed that the AI could consistently do this with another 10 per cent of the data, they tested the AI's performance using the last 20 per cent of the recordings.

Artificial Intelligence in Gastroenterology and Medicine, Health News, ET HealthWorld


We are surrounded by AI in our daily life whether it is the'Siri', 'Alexa', or the Google search engine. We have certainly come a long way from the'Turing test' in the 1950s, wherein the intelligent behaviour of computers was conceptualized leading to the present-day AI. The use of AI in Medicine (AIM) gained traction in the past decade and has met with both excitement in the scope of its use and also trepidation fearing the loss of the human element in the Art and Science of Medicine. Similar, to various subfields of Medicine the AI has its subfields of Machine learning (ML), Deep learning (DL) with Artificial Neural Network (ANN), Natural Language Processing (NLP), Computer Vision (CV). The use of AI in healthcare was slow to take off when compared to the non-medical commercial applications.

AI Capable of Early Detection of Pancreatic Cancer


Artificial intelligence (AI) deep learning is fast becoming a useful tool for detecting diseases from imaging data. A new study published in the journal Radiology shows how AI deep learning can detect pancreatic cancer on CT scans with 91 percent accuracy. "The deep learning–based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm," wrote the study authors affiliated with National Taiwan University. Pancreatic cancer is one of the deadliest cancers and the exact cause is not known. There were over 450,000 cases of pancreatic cancer and more than 430,000 deaths globally in 2018 according to GLOBOCAN.

AI helps detect pancreatic cancer


An artificial intelligence (AI) tool is highly effective at detecting pancreatic cancer on CT, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA). Pancreatic cancer has the lowest five-year survival rate among cancers. It is projected to become the second leading cause of cancer death in the United States by 2030. Early detection is the best way to improve the dismal outlook, as prognosis worsens significantly once the tumor grows beyond 2 centimeters. CT is the key imaging method for detection of pancreatic cancer, but it misses about 40% of tumors under 2 centimeters. There is an urgent need for an effective tool to help radiologists in improving pancreatic cancer detection.

Long Island doctors using AI to make colonoscopies more effective


Advances in artificial intelligence are making colonoscopies even more effective, as doctors on Long Island use what the call a "G.I. Genius" to help find abnormalities and possibly save patients' lives in the long run.. See more videos about Videos, Artificial Intelligence, Computer Science, Technology, New York City, Medical Technology.

Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors


Gastric cancer remains an enormous threat to human health. It is extremely significant to make a clear diagnosis and timely treatment of gastrointestinal tumors. The traditional diagnosis method (endoscope, surgery, and pathological tissue extraction) of gastric cancer is usually invasive, expensive, and time-consuming. The machine learning method is fast and low-cost, which breaks through the limitations of the traditional methods as we can apply the machine learning method to diagnose gastric cancer. This work aims to construct a cheap, non-invasive, rapid, and high-precision gastric cancer diagnostic model using personal behavioral lifestyles and non-invasive characteristics. A retrospective study was implemented on 3,630 participants. The developed models (extreme gradient boosting, decision tree, random forest, and logistic regression) were evaluated by cross-validation and the generalization ability in our test set. We found that the model developed using fingerprints based on the extreme gradient boosting (XGBoost) algorithm produced better results compared with the other models. The overall accuracy of which test set was 85.7%, AUC was 89.6%, sensitivity 78.7%, specificity 76.9%, and positive predictive values 73.8%, verifying that the proposed model has significant medical value and good application prospects.

Remote Computer Vision Engineer openings in California on August 14, 2022 – Data Science Jobs


Role requiring'No experience data provided' months of experience in None Samsara (NYSE: IOT) is the pioneer of the Connected Operations Cloud, which allows businesses that depend on physical operations to harness IoT (Internet of Things) data to develop actionable business insights and improve their operations. Founded in San Francisco in 2015, we now employ more than 1,800 people globally and have over 1.5 million active devices. Samsara also went public in December 2021 and we're just getting started. Recent awards we've won include: • #2 in the Financial Times' Fastest Growing Companies in Americas list 2021 • Named as a Best Place to Work in Built In 2022 • #19 in the Forbes Cloud 100 2021 • IoT Analytics Company of the Year in 2022's IoT Breakthrough Winners • Forbes Advisor named us the Best Solution for Large Companies – Fleet management software for 2022! We're driving change in industries that are yet to fully embrace digital transformation. Physical operations make up a massive slice of the global economy but haven't benefited from innovation and actionable information in the way that other sectors have.

Machine Learning Can Help Diagnose Alcohol-Associated Hepatitis from Other Conditions


Acute cholangitis is a potentially life-threatening bacterial infection that often is associated with gallstones. Symptoms include fever, jaundice, right upper quadrant pain, and elevated liver enzymes. While these may seem like distinctive, telltale symptoms, they are similar to those of a much different condition: alcohol-associated hepatitis. This challenges emergency department staff and other health care professionals who need to diagnose and treat patients with liver enzyme abnormalities and systemic inflammatory responses. New Mayo Clinic research finds that machine-learning algorithms can help health care staff distinguish the two conditions.

Artificial Intelligence-based smartphone app for characterizing stool form


In a recent study published in the American Journal of Gastroenterology, researchers at Cedars-Sinai Medical Center in the United States evaluated an artificial intelligence (AI)-based smartphone application (app) trained to assess a patient's stool characteristics. Study: A Smartphone Application Using Artificial Intelligence Is Superior To Subject Self-Reporting When Assessing Stool Form. Functional gastrointestinal (GI) disorders, especially luminal ones, require that a patient self-report stool form and frequency. However, since the symptoms of diarrhea common in irritable bowel syndrome with diarrhea (IBS-D) patients are subjective, the inability to accurately report or assess stool form and frequency makes it challenging to determine the effectiveness of therapeutic interventions in these conditions. The Bristol Stool Scale (BSS) is the United States Food and Drug Administration (US-FDA) approved 7-point scale that ranks stool consistency from 1 (hard lumps) to 7 (liquid).