Aidoc

Aidoc is an Israeli company that develops computer-aided simple triage systems.

Aidoc
IndustryMedical technology
Founded2016
FoundersElad Walach
Michael Braginsky
Guy Reiner
HeadquartersTel Aviv, Israel[1]
Websitewww.aidoc.com

The systems are in use in more than 300 medical centers, including Yale New Haven Hospital, University of Rochester Medical Center, Cedars-Sinai Medical Center and Sheba Medical Center.[2]

The FDA approved the systems for analysing scans of stroke, pulmonary embolism and cervical fracture.[2]

The pulmonary embolism system is CE marked.[3]

History

Aidoc was founded in 2016 by Elad Walach (CEO),[4] Michael Braginsky (CTO) and Guy Reiner (VP R&D).

In April 2017, the company raised $7M, led by TLV Partners.[5]

In April 2019, the company raised $27M, led by Squarepeg capital.[6] Additional investments make a total of $41.5M invested until April 2019.[7]

In August 2018, Aidoc gained FDA clearance for its intracranial hemorrhage system,[8] and in May 2019 it received clearance for the pulmonary embolism system.[9][10]

In January 2020, the system for detecting large-vessel occlusions (LVOs) in head CTA examinations got FDA clearance.[11][12][13]

In May 2020, the pulmonary embolism system was CE marked.[3]

Products and market

Aidoc has developed a suite of artificial intelligence products that flag both time-sensitive and time-consuming (for the radiologist) abnormalities across the body. The algorithms are developed with large quantities of data to provide diagnostic aid to a broad set of pathologies. The company offers an array of algorithms that span across the body including Intracranial hemorrhage, spine fractures (C, T & L), free air in the abdomen, pulmonary embolism and more. It developed "Always-on AI", a term coined by Elad Walach which refers to a type of artificial intelligence that is "Always-on" - constantly running in the background and automatically analyzing medical imaging data, identifying urgent findings and sparing radiologists from "drowning" in vast amounts of irrelevant data.[14][15]

Aidoc's solutions cover medical conditions prevalent in all settings (ED/inpatient/outpatient), including level 1 trauma centers, outpatient imaging centers, teleradiology groups and are set up in over 200 medical centers worldwide. Notable customers include the University of Rochester Medical Center,[16] Global Diagnostics Australia,[17] Antwerp University Hospital, and AZ Maria Middelares hospital. According to the company, Aidoc has deep integrations with numerous PACS and workflow software providers (GE, Agfa, Nuance).

Clinical Research

A clinical study on Aidoc’ accuracy of deep convolutional neural networks for the detection of pulmonary embolism (PE) on CT pulmonary angiograms (CTPAs) was performed by the University Hospital of Basel and presented at the European Congress of Radiology, showing that the Aidoc algorithm reached 93% sensitivity and 95% specificity.[18][19] [20] Clinical research has also been performed to test the diagnostic performance of Aidoc's deep learning-based triage system for the flagging of acute findings in abdominal computed tomography (CT) examinations. Overall, the algorithm achieved a 93% sensitivity (91/98, 7 false-negative) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings.[21][22]

Additional clinical research on Aidoc's Intracranial Hemorrhage algorithm accuracy was presented at the European Congress of Radiology by Antwerp University Hospital, evaluating the use of its deep learning algorithm for the detection of intracranial hemorrhage on non-contrast enhanced CT of the brain. Results showed that use of a deep learning algorithm for the detection of pathological intracranial hyperdensities helped to detect urgent cases more quickly.[23] The University of Washington completed a study on the accuracy of Aidoc's intracranial hemorrhage algorithm on 7112 non-contrast head CTs acquired from two large urban academic and trauma centers, concluding that the algorithm reached 98% accuracy, with 95% sensitivity and over 98% specificity.[24]

References
  1. "AIDoc Medical Ltd". Bloomberg.
  2. Tel Aviv start-up gets FDA approval for ‘stroke of genius’ AI package, The Jerusalem Post, January 13, 2020
  3. AI solution for incidental pulmonary embolism given CE mark, med-technews.com, May 4, 2020
  4. "Global Diagnostics Australia Incorporates Artificial Intelligence Into Its Radiology Applications". Yahoo Finance. August 8, 2019. Retrieved November 22, 2019.
  5. AIDoc Medical raises $7M to bring AI to medical imaging analysis, TechCrunch, April 26, 2017
  6. "Aidoc, the AI solution for medical imaging analysis, raises $27M Series B – TechCrunch". TechCrunch. 17 April 2019.
  7. Aidoc, Crunchbase
  8. "August 2018 510(k) Clearances". U.S. Food and Drug Administration. 7 September 2018.
  9. "The meaning of regulatory approval for AI". AI Med. 15 May 2019.
  10. "FDA OKs World's First AI Solution for Flagging Pulmonary Embolism". Medical Product Outsourcing. 16 May 2019.
  11. "Aidoc's AI solution for LVOs gains FDA clearance". AI in Healthcare. January 13, 2020. Retrieved January 22, 2020.
  12. "Medical Imaging Startup Aidoc Gets FDA Clearance For AI Solution To Spot Stroke". NoCamels Innovation News. 13 January 2020. Retrieved 11 March 2020.
  13. Palmer, Whitney J. (13 January 2020). "AI Start-Up Receives FDA Clearance for Comprehensive CT Stroke Package". MJH Life Sciences. Retrieved 10 April 2020.
  14. Blum, Brian; Leichman, Abigail Klein (11 December 2018). "AI tool helps radiologists clear dangerous data bottleneck". Israel21c.
  15. "Aidoc releases complete AI package for treatment of stroke". NeuroNews International. 23 September 2019.
  16. "Nuance's AI Marketplace Delivers AI at Scale with Industry's First Workflow-Integrated Market for Diagnostic Imaging Algorithms". Nuance Communications. 26 November 2018.
  17. "Global Diagnostics Australia incorporates artificial intelligence into its radiology applications". Global Diagnostics Australia. 8 August 2019.
  18. "Assessment of Artificial Intelligence Technology for Pulmonary Embolism Detection".
  19. "AI-powered detection of pulmonary embolism in CT pulmonary angiograms: a validation study of the diagnostic performance of prototype algorithms". Aidoc.
  20. "Detection of intracranial haemorrhage on CT of the brain using a deep learning algorithm". Aidoc.
  21. Winkel, DJ; Heye, T; Weikert, TJ; Boll, DT; Stieltjes, B. (20 November 2019). "Evaluation of an AI-Based Detection Software for Acute Findings in Abdominal Computed Tomography Scans: Toward an Automated Work List Prioritization of Routine CT Examinations". Investigative Radiology. 54 (1): 55–59. doi:10.1097/RLI.0000000000000509. PMID 30199417.
  22. Winkel, D. J.; Heye, T.; Weikert, T. J.; Boll, D. T.; Stieltjes, B. (20 November 2019). "Evaluation of an AI-Based Detection Software for Acute... : Investigative Radiology". Investigative Radiology. 54 (1): 55–59. doi:10.1097/RLI.0000000000000509. PMID 30199417.
  23. "Preliminary Results of Aidoc's Deep Learning Algorithm Detection Accuracy for Pathological Intracranial Hyperdense Lesions". Aidoc.
  24. P. Ojeda; M. Zawaideh; M. Mossa-Basha; D. Haynor (2019). "The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies". In Angelini, Elsa D; Landman, Bennett A (eds.). Medical Imaging 2019: Image Processing. p. 128. doi:10.1117/12.2513167. ISBN 9781510625457.CS1 maint: multiple names: authors list (link)
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