Deep learning has the potential to help doctors cut down on diagnostic errors, said cardiologist Rima Arnaout in a talk at the GPU Technology Conference.
An assistant professor of medicine at the University of California, San Francisco, Arnaout is focusing on the potential of AI to analyze cardiac ultrasounds and detect congenital heart disease from fetal ultrasounds.
“In medicine, a picture is worth more than a thousand words,” she said. “It really can be worth a patient’s life, in some cases.”
What Can AI Do to Help?
Arnaout outlined a few key challenges for humans analyzing medical images. For one, people sometimes make mistakes. There’s also a physical limit to how much data cardiac imaging specialists like cardiologists and radiologists can analyze.
“We cannot allow those kinds of shortcomings,” she said. “We need accuracy, precision, and we need it delivered at scale.”
While AI models are not without their limitations, Arnaout said, they can help clinicians use medical imaging techniques like ultrasound to their full potential.
She turned to echocardiogram data because “it’s balanced in terms of information richness and clinical volume compared to other cardiovascular imaging tools.” Since echocardiograms can be used for the diagnosis and management of almost every cardiovascular disease, she said, there’s very little selection bias in the datasets.
Echocardiograms are a challenging training dataset, however, because one ultrasound study consists of still images and videos captured from over a dozen angles. A study Arnaout’s team published in npj Digital Medicine used deep learning to classify 15 of these standard views, achieving 91.7 percent accuracy on low-resolution images.
"Kutumia teknolojia ya ""Deep Learning"" inaweza kusaidia waganga kupunguza makosa ya utambuzi, alisema mtaalamu wa moyo Rima Arnault katika hotuba yake katika Mkutano wa Teknolojia ya GPU." Profesa wa matibabu katika Chuo Kikuu cha California, San Francisco, Arnault anaangalia uwezo wa AI kuchanganua ultrasounds ya moyo na kugundua ugonjwa wa moyo wa kuzaliwa kutoka kwa ultrasounds ya kiinitete. "Katika tiba, picha ina thamani ya maneno zaidi ya elfu moja", akasema. "Ni kweli inaweza kuwa na thamani ya maisha ya mgonjwa, katika baadhi ya kesi. ""Ni nini AI inaweza kufanya ili kusaidia?" Arnaout alielezea changamoto chache muhimu kwa wanadamu kuchambua picha za matibabu. Kwanza, nyakati nyingine watu hufanya makosa. Pia kuna kikomo cha kimwili kwa kiasi cha data wataalamu wa picha za moyo kama wataalamu wa moyo na wataalamu wa mionzi wanaweza kuchambua. "Hatuwezi kuruhusu aina hizo za mapungufu", alisema. "Tunahitaji usahihi, usahihi, na tunahitaji kutolewa kwa kiwango. ""Wakati mifano ya AI haiko bila mapungufu yao, Arnault alisema, wanaweza kusaidia madaktari kutumia mbinu za upigaji picha za matibabu kama vile ultrasound kwa uwezo wao kamili." "Kama ilivyoelezwa hapo juu, echocardiogram ni ""kiwango cha juu cha data ya moyo na uwezo wa kugundua athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari za athari.""" Uchunguzi wa echocardiogram ni muhimu sana kwa sababu inaonyesha picha za picha za picha na video zilizochukuliwa kutoka pembe nyingi. Katika utafiti uliochapishwa katika jarida la NPR Digital Medicine, timu ya Arnault ilitumia kujifunza kwa kina kuorodhesha maoni 15 ya kawaida, ikifikia usahihi wa asilimia 91.7 kwenye picha za azimio la chini.