Venus Bliss is cleared by the FDA and licensed by Health Canada for non-invasive lipolysis of the abdomen and flanks in individuals with a Body Mass Index (BMI) of 30 or less, with the diode laser applicators. The (MP)2 applicator is cleared by the FDA for temporary reduction in the appearance of cellulite, and licensed by Health Canada for temporary increase of skin tightening, temporary circumferential reduction, and temporary cellulite reduction. Venus Bliss has CE Mark as a non-invasive medical aesthetic device enabling a comprehensive approach leading to body contouring, addressing fat reduction, skin tightening, circumference reduction, and cellulite reduction. Venus Versa is cleared by the FDA, licensed by Health Canada, and has CE Mark as a multi-application device intended to be used in aesthetic and cosmetic procedures. The SR515 and SR580 applicators are cleared by the FDA, licensed by Health Canada, and have CE Mark for the treatment of benign pigmented epidermal and cutaneous lesions and treatment of benign cutaneous vascular lesions.
Researchers from the Icahn School of Medicine at Mount Sinai developed an artificial intelligence model that can scan physicians' notes and distinguish between acute and chronic lower back pain, according to findings published in the Journal of Medical Internet Research. "Several studies have documented increases in medication prescriptions and visits to physicians, physical therapists, and chiropractors for lower back pain episodes," Ismail Nabeel, MD, MPH, associate professor of environmental medicine and public health at the Icahn School of Medicine at Mount Sinai, said in a press release. "This study is important because artificial intelligence can potentially more accurately distinguish whether the pain is acute or chronic, which would determine whether a patient should return to normal activities quickly or rest and schedule follow-up visits with a physician." "This study also has implications for diagnosis, treatment and billing purposes in other musculoskeletal conditions, such as the knee, elbow, and shoulder pain, where the medical codes also do not differentiate by pain level and acuity," he added. To examine the feasibility of a system that automatically distinguishes acute lower back pain based on free-text clinical notes, Nabeel and colleagues used a dataset of 17,409 clinical notes from various primary care practices in the Mount Sinai Health System.
In a country with more than 230 million vehicles and half a million auto accidents every year, scheduling damage inspections can keep cars and policyholders off the road for days or longer. A more convenient way was needed. To ease the pain, ICICI Lombard partnered Microsoft to develop India's first AI-enabled car inspection feature in its mobile app, "Insure." Policyholders can simply take images of their vehicle and upload them to the app. AI analyses the images, identifies damages and provides an estimated repair cost.
BROOKLYN, New York, Wednesday, March 4, 2020 - Robots hold promise for a large number of people with neurological movement disorders severely affecting the quality of their lives. Now researchers have tapped artificial intelligence techniques to build an algorithmic model that will make the robots more accurate, faster, and safer when battling hand tremors. Their model, which is ready for others to deploy, appears this month in Scientific Reports, an online journal of Nature. The international team reports the most robust techniques to date to characterize pathological hand tremors symptomatic of the common and debilitating motor problems affecting a large number of aging adults. One million people throughout the world have been diagnosed with Parkinson's disease, just one of the neurodegenerative diseases that can cause hand tremors.
About 80 percent of adults experience lower back pain in their lifetime; it is the most common cause of job-related disability. Many argue that prescribing opioids for lower back pain contributed to the opioid crisis; thus, determining the quality of lower back pain in clinical practice could provide an effective tool not only to improve the management of lower back pain but also to curb unnecessary opioid prescriptions. Acute and chronic lower back pain are different conditions with different treatments. However, they are coded in electronic health records with the same code and can be differentiated only by retrospective reviews of the patient's chart, which includes the review of clinical notes. The single code for two different conditions prevents appropriate billing and therapy recommendations, including different return-to-work scenarios.
Rina Cummings has worked three 12-hour shifts every week at Amazon's gargantuan New York City warehouse, called JFK8, on Staten Island since it first began operations in late 2018. As a sorter on the outbound ship dock, her job is to inspect and scan a mandated rate of 1,800 Amazon packages an hour – 30 per minute – that are sent through a chute and transported on a conveyor belt before leaving the facility for delivery. Workers such as Cummings helped Amazon achieve its best ever Christmas this year. Faster shipping drove Amazon's revenues to $87bn for fourth quarter of 2019, adding another $12.8bn to founder Jeff Bezos's $128.9bn Amazon has just signed a deal to take another 450,000 sq ft of warehouse space on the island to speed delivery to its New York-area consumers.
A startup from the University of California-Los Angeles (UCLA) is developing an AI-based tool that analyzes spine images to inform patients whether or not they need surgery. Based on research conducted by UCLA neurosurgeon Dr. Luke Macyszyn, the startup Theseus AI aims to address the costly medical problem of unnecessary spine surgeries. Studies show that between 20 and 40 percent of spine surgeries fail to relieve pain, and there are more than 250,000 performed each year. "This is somewhat personal for me in that my own father had spine surgery twice," says Sam Elhag, CEO of Theseus AI. "To this date, it's uncertain as to whether or not it all made sense." Elhag launched Theseus to make the interpretation of spine MRIs less subjective.
Artificial intelligence is a much-discussed topic in medicine, especially in the field of diagnostics. "We aimed to investigate the potential on the basis of a specific example," explains Prof. Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "Because this requires large amounts of data, we evaluated data on the gene activity of blood cells. Numerous studies have been carried out on this topic and the results are available through databases. Thus, there is an enormous data pool. We have collected virtually everything that is currently available."
The hope of The Human Genome Project was that it would herald a new age of precision medicine. However, the challenge turned out to be more complex and nuanced than had been imagined. Of nearly 25,000 human genes, only 2,418 have been associated with specific diseases, explaining only a small fraction of all human pathologies. In 2020, we will begin to harness the power of artificial intelligence (AI) to create new, life-saving medicine. In the past decade, we have learned a great deal about the complexity of diseases.
In the largest metastudy to date on acute myeloid leukemia, German researchers contend that they have demonstrated that artificial intelligence can detect this common and deadly form of blood cancer. Results of their proof-of-concept study, published in the journal iScience, are based on the analysis of the gene activity of cells found in blood using 12,029 samples from 105 different studies. "Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omics approach where risk prediction, differential diagnosis and subclassification of AML is achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning," state the study's authors. "The transcriptome holds important information about the condition of cells," says Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "However, classical diagnostics is based on different data. We therefore wanted to find out what an analysis of the transcriptome can achieve using artificial intelligence--that is to say trainable algorithms."