Israeli radiology startup Aidoc has received FDA clearance for its AI-based product meant to help identify potential cases of pulmonary embolism in chest CT scans. Pulmonary embolism (PE) – which occurs when a blood clot gets lodged in the lung – is considered a silent killer that causes up to 200,000 deaths a year in the United States. The condition often strikes with little to no warning and diagnosis of a case can be extremely time-sensitive. Aidoc's technology doesn't require dedicated hardware and runs continuously on hospital systems, automatically ingesting radiological images. The 70-person company focuses on workflow optimization in radiology to help triage high risk patients for additional and faster review.
Pulmonary embolisms (PE) are known to be one of the leading causes for cardiac-related mortality. Due to inherent variabilities in how PE manifests and the cumbersome nature of manual diagnosis, there is growing interest in leveraging AI tools for detecting PE. In this paper, we build a two-stage detection pipeline that is accurate, computationally efficient, robust to variations in PE types and kernels used for CT reconstruction, and most importantly, does not require dense annotations. Given the challenges in acquiring expert annotations in large-scale datasets, our approach produces state-of-the-art results with very sparse emboli contours (at 10mm slice spacing), while using models with significantly lower number of parameters. We achieve AUC scores of 0.94 on the validation set and 0.85 on the test set of highly severe PEs. Using a large, real-world dataset characterized by complex PE types and patients from multiple hospitals, we present an elaborate empirical study and provide guidelines for designing highly generalizable pipelines.
Aidoc, which bills itself as an AI solutions provider for radiologists, today closed a $20 million extension to the series B it raised in April 2019, bringing the round total to $47 million and the company's total raised to $60 million. Cofounder and CEO Elad Walach says the money will be used to support new customers after revenue tripled from the beginning of 2020. Computer vision holds promise for the $6.5 trillion medical diagnostics industry, as highlighted by a 2018 paper in the journal Nature that found that some algorithms can identify skin cancer as accurately as a panel of doctors. For instance, Sight Diagnostics uses machine learning algorithms to perform point-of-care complete blood count (CBC) tests within 10 minutes with no more than a pinprick of blood. Aidoc got its start in 2016, when veterans of the Israeli Defense Force put their heads together to create an AI platform targeting certain health care verticals.
Aidoc, the leading provider of AI solutions for radiologists, today announced that the US Food and Drug Administration (FDA) has cleared its AI solution for flagging Large-Vessel Occlusion (LVO) in head CTA scans, marking Aidoc's fourth FDA-cleared AI package. Combined with Aidoc's previously-cleared AI module for flagging and prioritizing intracranial hemorrhage, together they provide a comprehensive AI package for the identification and triage of both ischemic and hemorrhagic stroke in CTs, speeding time to treatment when every minute counts. "Stroke is the ultimate time-critical condition," said Dr. Marcel Maya, Co-chair Department of Imaging, Cedars-Sinai Medical Center. "The faster we can identify, diagnose and treat it, the better the outcome for patients. Aidoc's comprehensive stroke package flags both large vessel occlusion and hemorrhages inside our existing workflows, ensuring we can diagnose stroke faster and decide on the best course of treatment. We're already seeing how this has a positive impact on department efficiency and patient length of stay."