Evaluation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI.

Autor: Goss DM; School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; IVFAustralia, Sydney, New South Wales, Australia., Vasilescu SA; School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia., Vasilescu PA; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia., Cooke S; IVFAustralia, Sydney, New South Wales, Australia., Kim SH; IVFAustralia, Sydney, New South Wales, Australia.; University of New South Wales, Sydney, New South Wales, Australia., Sacks GP; School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; IVFAustralia, Sydney, New South Wales, Australia.; University of New South Wales, Sydney, New South Wales, Australia., Gardner DK; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; Melbourne IVF, Melbourne, Victoria, Australia., Warkiani ME; School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, New South Wales, Australia.. Electronic address: majid.warkiani@uts.edu.au.
Jazyk: angličtina
Zdroj: Reproductive biomedicine online [Reprod Biomed Online] 2024 Jul; Vol. 49 (1), pp. 103910. Date of Electronic Publication: 2024 Feb 22.
DOI: 10.1016/j.rbmo.2024.103910
Abstrakt: Research Question: Can artificial intelligence (AI) improve the efficiency and efficacy of sperm searches in azoospermic samples?
Design: This two-phase proof-of-concept study began with a training phase using eight azoospermic patients (>10,000 sperm images) to provide a variety of surgically collected samples for sperm morphology and debris variation to train a convolutional neural network to identify spermatozoa. Second, side-by-side testing was undertaken on two cohorts of non-obstructive azoospermia patient samples: an embryologist versus the AI identifying all the spermatozoa in the still images (cohort 1, n = 4), and a side-by-side test with a simulated clinical deployment of the AI model with an intracytoplasmic sperm injection microscope and the embryologist performing a search with and without the aid of the AI (cohort 2, n = 4).
Results: In cohort 1, the AI model showed an improvement in the time taken to identify all the spermatozoa per field of view (0.02 ± 0.30  ×  10 -5 s versus 36.10 ± 1.18s, P < 0.0001) and improved recall (91.95 ± 0.81% versus 86.52 ± 1.34%, P < 0.001) compared with an embryologist. From a total of 2660 spermatozoa to find in all the samples combined, 1937 were found by an embryologist and 1997 were found by the AI in less 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 found without AI, although no significant difference was observed.
Conclusions: AI-powered image analysis has the potential for seamless integration into laboratory workflows, to reduce the time to identify and isolate spermatozoa from surgical sperm samples from hours to minutes, thus increasing success rates from these treatments.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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