An artificial intelligence machine learning model that mines electronic health record data could help physicians fight the opioid epidemic by targeting non-opioid pain treatments to patients experiencing severe pain after surgery, according to new research presented at the Anesthesiology 2020 annual meeting.

LONDON – French venture capital firm Kurma Partners has announced the first closing at €50 million (US$58.6 million) of its second fund to be wholly devoted to seed and early stage investment in diagnostics. Kurma Diagnostics 2 (KDx2) will be invested in molecular and digital diagnostics technologies being spun out of universities and research centers across Europe, backing a total of 15 companies.

PARIS – Gleamer SAS has secured $8.75 million in series A financing for its CE-marked artificial intelligence (AI) application that automatically detects all types of fracture in radiography. “This funding will allow us to move forward with launching Boneview around the world,” Christian Allouche, CEO and co-founder of Gleamer, told BioWorld.

HONG KONG – Novartis AG was cited during the recent 2020 KoNECT-MOHW-MFDS International Conference as an example of a biopharma firm staying ahead of the artificial intelligence curve. During this week’s 2020 Bioplus Interphex Korea conference, Won Kim, head of innovation at Novartis Korea, described some of the firm’s latest forays into AI, including negotiations with potential partners to develop an AI image analysis solution for retinal diseases.

Children with autism spectrum disorder (ASD) can face a lifetime of frustration because of challenges with communication, social behaviors and flexibility of thought. Early intervention can improve outcomes, but nailing a diagnosis of ASD often takes years. Cognoa Inc. wants to change that with its digital ASD Diagnostic and is on track to apply for U.S. FDA clearance before the end of the year.

Two studies seek to answer the most pressing question for physicians examining a patient with COVID-19: What's this person's risk of death? Mount Sinai researchers presented their clinical prediction model in The Lancet Digital Health and a team from Johns Hopkins published their risk calculator in the Annals of Internal Medicine.

The field of artificial intelligence (AI) is stretching the boundaries of conventional med-tech regulation, and several regulatory agencies are working to cut that Gordian knot. Marc Lamoureaux, director of digital health at Health Canada’s (HC) medical device directorate, said on a Sept. 21 webinar that legislation passed in 2019 gives the agency a “regulatory sandbox” in which to experiment with AI regulation, a mechanism he said may bring these algorithms to market much more rapidly than would otherwise be the case.

The digital medicine company Physiq Inc. has received a contract from the NIH to develop an artificial intelligence (AI)-based index that can provide an early warning that a patient with COVID-19 is in decline and needs medical treatment. The index, called the COVID-19 Decompensation Index (CDI) Digital Biomarker, will run on Physiq’s existing Accelerateiq computing platform, analyzing physiological data from FDA-cleared wearable devices to create a personalized baseline for patients who have tested positive for COVID-19.

Israeli startup Olive Diagnostics Pvt. Ltd. is aiming to disrupt at-home urinalysis testing with a hands-free, noninvasive, artificial intelligence (AI)-based solution that provides remote diagnostics by detecting urine’s molecular composition. K2, as the device is called, attaches easily to the toilet rim to generate secure, personalized diagnostic data that directly links to a mobile app.

TORONTO – “With COVID-19 it’s not been an easy time to raise capital.” In virtually the same breath Joshua Liu, CEO of Toronto-based health-tech startup SeamlessMD Inc. credited the pandemic with nudging investors to spend CA$4 million (US$3 million) to grow his company’s cloud-based patient engagement app, boosting a library of digital plans for surgery, cancer and chronic care and adding machine learning-based risk prediction to remote patient monitoring.