首页|University of Technology Sydney Reports Findings in Artificial Intelligence (Eva luation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI)
University of Technology Sydney Reports Findings in Artificial Intelligence (Eva luation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Artificial Intelligenc e is the subject of a report. According to newsreporting out of Sydney, Austral ia, by NewsRx editors, research stated, “Can artificial intelligence (AI)improv e the efficiency and efficacy of sperm searches in azoospermic samples? This two -phase proofof-concept study began with a training phase using eight azoospermi c patients (>10,000 sperm images)to provide a variety o f surgically collected samples for sperm morphology and debris variation to trai na convolutional neural network to identify spermatozoa. Second, side-by-side t esting was undertaken ontwo cohorts of non-obstructive azoospermia patient samp les: an embryologist versus the AI identifyingall the spermatozoa in the still images (cohort 1, n = 4), and a side-by-side test with a simulated clinicaldepl oyment of the AI model with an intracytoplasmic sperm injection microscope and t he embryologistperforming a search with and without the aid of the AI (cohort 2 , n = 4).”Our news journalists obtained a quote from the research from the University of T echnology Sydney,“In cohort 1, the AI model showed an improvement in the time t aken to identify all the spermatozoa perfield of view (0.02 ± 0.30 x 10s versus 36.10 ± 1.18s, P<0.0001) and improved recall (91.95 ± 0.8 1%versus 86.52 ± 1.34%, P<0.00 1) compared with an embryologist. From a total of 2660 spermatozoa tofind in al l the samples combined, 1937 were found by an embryologist and 1997 were found b y the AI inless than 1000th of the time. In cohort 2, the AI-aided embryologist took significantly less time per droplet(98.90 ± 3.19 s versus 168.7 ± 7.84 s, P<0.0001) and found 1396 spermatozoa, while 1274 were fou ndwithout AI, although no significant difference was observed.”
SydneyAustraliaAustralia and New ZealandArtificial IntelligenceEmerging TechnologiesMachine Learning
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