Elad Walach

AI
3 predictions for radiology AI in 2021
With virtual RSNA 2020 over and the year drawing to a close, Elad Walach of artificial intelligence (AI) developer Aidoc has taken the time to share his predictions about trends in the world of radiology AI for next year.
December 14, 2020
2019 11 14 18 07 2163 Computer Doctor Future 400
AI
5 reasons why imaging AI is different from CAD
Artificial intelligence (AI) in medical imaging is on the rise and already becoming part of the standard of care. But with all the hype, one must ask: Is next-generation imaging AI simply a new take on computer-aided detection/diagnosis (CAD)? Aidoc CEO Elad Walach presents five reasons why AI is fundamentally different.
October 28, 2018
2017 05 09 11 15 29 355 Cyber Informatics 400
AI
3 ways to ensure AI algorithms are really working
Artificial intelligence (AI), deep learning, and related technologies have emerged as solutions that can really make a difference in almost every medical field imaginable, from radiology to pharmaceuticals to diagnosis. But the question remains: How do we know that these algorithms are actually working?
August 5, 2018
2016 10 03 14 26 17 320 Computer Binary 400
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By Kate Madden Yee
The findings highlight the need for systematic dose tracking and rigorous justification of imaging.
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Study questions routine use of radiotherapy after bone surgery
By Will Morton
Findings suggest a possible hazard associated with postoperative radiotherapy.
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Lower limb amputation is an increasingly common major complication of advanced peripheral arterial disease and diabetes mellitus.
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Could AI scoring help with managing DCIS?
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Images of a 39-year-old who underwent screening mammography. Right craniocaudal (left) and mediolateral oblique (middle) views from preoperative mammography show suspicious grouped fine pleomorphic calcifications (arrows). After biopsy showed ductal carcinoma of situ (DCIS), patient underwent breast-conserving surgery, yielding final diagnosis of ductal carcinoma in situ (intermediate grade, hormone receptor positive). (Right) Screenshot of an output of a commercial AI tool applied to preoperative mammography. AI detected right breast lesion with score of 95% on both views (concentric colored circles). Present study’s reviewing radiologist deemed AI marking to correspond with site of DCIS.
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Axial contrast-enhanced CT scan in a 28-year-old premenopausal woman shows an incidentally detected solid-appearing left ovarian lesion (solid arrow) anterior to the uterus (U), associated with trace ascites in the right adnexa and cul de sac (dashed arrows). At pathologic analysis, this was shown to be ovarian dysgerminoma. Two readers misdiagnosed this lesion, one as a leiomyoma and one as an ovarian fibroma.
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Super-resolution deep learning reconstruction improves CCTA
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Images from two representative cases. (A-C) Images in an 82-year-old man with a calcified plaque in the proximal segment of the left anterior descending artery. (A, B) Curved planar reconstruction images from CCTA with (A) hybrid iterative reconstruction (HIR) and (B) super-resolution deep learning reconstruction (SR-DLR) show that the measured PDS was 66% for the HIR image and 42% for the SR-DLR image. (C) Quantitative analysis of the invasive coronary angiography (ICA) image (reference standard) revealed a PDS of 34% (arrow). (D-F) Images in a 37-year-old woman with a noncalcified plaque in the middle segment of the left circumflex artery. (D, E) Curved planar reconstruction images from CCTA with (D) HIR and (E) SR-DLR show that the measured PDS was 47% for the HIR image and 80% for the SR-DLR image. (F) Quantitative analysis of the ICA image revealed a PDS of 72% (arrow). Blue, red, and yellow lines in A, B, D, and E represent the proximal reference point, stenosed lesion, and distal reference point, respectively, and the insets show the corresponding cross-sectional images.
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