Pune (Maharashtra) [India], January 5: DeepTek, a leading health tech startup in the Radiology AI space announced the global launch of DeepSpine – an AI powered lumbar spine quantification and metrics tool designed to analyze MRI lumbar spine images. The tool was launched at the Radiological Society of North America (RSNA) annual meeting in Chicago, USA.
DeepTek has been instrumental in transforming the radiology workflow by leveraging the power of AI. Its AI-powered radiology orchestration solution Augmento has already created waves across the Asia Pacific and is also getting deployed in a progressive Singapore health system. Another flagship product by DeepTek is Genki – the AI-powered public health screening solution which is helping medical professionals across India, Philippines, Mongolia, and several other countries in the APAC region to eradicate TB by identifying suspected cases promptly and precisely, leading to earlier treatment and better patient care.
DeepTek’s new innovation – DeepSpine provides AI powered quantification and analysis tool for lumbar spine MRI studies and can assist radiologists, technologists, orthopedic surgeons and physio experts to get objective automated quantification and other document of lumbar spine, adding value to the radiology reports and decongesting the radiology workflow.
Lower back pain (LBP) affects up to 80%–85% of the population during their lifetime. It is a leading cause of morbidity and disability, with an increasing prevalence due to the steadily aging population worldwide. According to the American College of Radiologists, lumbar spine MRI is the preferred imaging modality to rule out causes of complicated lower back pain and to decide whether conservative or invasive therapeutic approaches should be considered. Subsequently, the number of MRI studies of the lumbar spine has been rising over the last decades at a much higher rate than the number of trained radiologists who could adequately interpret the MR images.
Dr. Amit Kharat – Radiologist and Co-Founder of DeepTek says, “The lumbar spine MRI quantification and metrics tool can derive metrics from MRI studies such as Vertebral Body Heights, Disc Bulge Classification, Foraminal Stenosis, Listhesis, Canal Stenosis and various spine angles. This can help get deep insights in treatment planning and progress monitoring. The tool can be a great value add to the ecosystem of health care workers managing low back pain”.
DeepTek (www.deeptek.ai) is amongst very few Radiology AI start-ups, which has successfully established commercial adoption of AI technology in clinical practice–creating clear and quantifiable value for patients, hospitals, and radiologists. DeepTek’s offerings are used by over 350+ global customers spread across India, Japan, Singapore, Thailand, Philippines, Nigeria, Kenya, and several other countries. DeepTek’s offering touches over 60,000 lives every month.
DeepTek has a team of 170+ members with a unique mix of technology experts and radiologists. It also has strategic equity investment from TATA Capital Healthcare Fund II, NTT DATA (a leading global IT innovator with a large medical imaging business in the USA), Nobori Ltd. (Japan’s leading Radiology solutions company), Doctor-Net Inc. (Japan’s largest teleradiology company) and a few other institutional investors. In a short span, the work done at DeepTek has been presented at global radiology events, and resulted in research publications with more than 200+ citations. DeepTek has already filed for 6 global patents and is working on a few more.
RSNA 2022 Scientific Assembly and annual meeting organized by Radiological Society of North America (RSNA) welcomed radiologists from around the world to take part in their scientific and educational programs. RSNA is a non-profit organization with over 54,000 members from 136 countries around the world. RSNA promotes excellence in patient care and health care delivery through education, research and technological innovation. RSNA offers patient-centered care resources to enhance your skills and empower your patients to make better informed decisions about their health care.
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Divya Agarwal
