{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:29:28Z","timestamp":1791419368532,"version":"4.3.3"},"reference-count":16,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Sperm identification and selection is an essential task when processing human testicular samples for\n                      <jats:italic toggle=\"yes\">in vitro<\/jats:italic>\n                      fertilization. Locating and identifying sperm cell(s) in human testicular biopsy samples is labor intensive and time consuming. We developed a new computer-aided sperm analysis (CASA) system, which utilizes deep learning for near human-level performance on testicular sperm extraction (TESE), trained on a custom dataset. The system automates the identification of sperm in testicular biopsy samples. A dataset of 702 de-identified images from testicular biopsy samples of 30 patients was collected. Each image was normalized and passed through glare filters and diffraction correction. The data were split 80%, 10%, and 10% into training, validation, and test sets, respectively. Then, a deep object detection network, composed of a feature extraction network and object detection network, was trained on this dataset. The model was benchmarked against embryologists' performance on the detection task. Our deep learning CASA system achieved a mean average precision (mAP) of 0.741, with an average recall (AR) of 0.376 on our dataset. Our proposed method can work in real time; its speed is effectively limited only by the imaging speed of the microscope. Our results indicate that deep learning-based technologies can improve the efficiency of finding sperm in testicular biopsy samples.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/aja.aja_66_20","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T11:47:49Z","timestamp":1603367269000},"page":"135-139","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":35,"title":["A preliminary study of sperm identification in microdissection testicular sperm extraction samples with deep convolutional neural networks"],"prefix":"10.4103","volume":"23","author":[{"given":"Daniel J","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Odgerel","family":"Badamjav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vikrant V","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Eisenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barry","family":"Behr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"R1-3-20240829","doi-asserted-by":"crossref","first-page":"27","DOI":"10.6061\/clinics\/2013(Sup01)04","article-title":"Update in the evaluation of the azoospermic male","volume":"68","author":"Gudeloglu","year":"2013","journal-title":"Clinics"},{"key":"R2-3-20240829","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1093\/HUMREP\/10.6.1457","article-title":"Pregnancies after testicular sperm extraction and intracytoplasmic sperm injection in non-obstructive azoospermia","volume":"10","author":"Devroey","year":"1995","journal-title":"Hum Reprod"},{"key":"R3-3-20240829","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1093\/humrep\/14.1.131","article-title":"Testicular sperm extraction: microdissection improves sperm yield with minimal tissue excision","volume":"14","author":"Schlegel","year":"1999","journal-title":"Hum Reprod"},{"key":"R4-3-20240829","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1093\/humupd\/dmz028","article-title":"Sperm recovery and ICSI outcomes in men with non-obstructive azoospermia: a systematic review and meta-analysis","volume":"25","author":"Corona","year":"2019","journal-title":"Hum Reprod Update"},{"key":"R5-3-20240829","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1093\/humupd\/dmm029","article-title":"Which is the best sperm retrieval technique for non-obstructive azoospermia. A systematic review?","volume":"13","author":"Donoso","year":"2007","journal-title":"Hum Reprod Update"},{"key":"R6-3-20240829","doi-asserted-by":"crossref","first-page":"125","DOI":"10.6061\/clinics\/2013(Sup01)14","article-title":"Laboratory processing and intracytoplasmic sperm injection using epididymal and testicular spermatozoa: what can be done to improve outcomes?","volume":"68","author":"Popal","year":"2013","journal-title":"Clinics"},{"key":"R7-3-20240829","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1093\/humrep\/15.11.2360","article-title":"Intra- and inter-individual variability in human sperm concentration, motility and vitality assessment during a workshop involving ten laboratories","volume":"15","author":"Auger","year":"2000","journal-title":"Hum Reprod"},{"key":"R8-3-20240829","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.fertnstert.2015.08.036","article-title":"Inherent difficulties of meta-analysis for surgical techniques in male infertility: an argument for standardizing reporting and outcomes","volume":"104","author":"Anderson","year":"2015","journal-title":"Fertil Steril"},{"key":"R10-3-20240829","doi-asserted-by":"crossref","first-page":"3289801","DOI":"10.1155\/2016\/3289801","article-title":"Deep neural networks-based recognition of plant diseases by leaf image classification","volume":"2016","author":"Sladojevic","year":"2016","journal-title":"Comput Intell Neurosci"},{"key":"R13-3-20240829","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"R17-3-20240829","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"R18-3-20240829","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/TMI.2016.2528821","article-title":"Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation","volume":"35","author":"Brosch","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"R19-3-20240829","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","article-title":"Lung pattern classification for interstitial lung diseases using a deep convolutional neural network","volume":"35","author":"Anthimopoulos","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"R20-3-20240829","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"35","author":"Pereira","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"R21-3-20240829","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TMI.2016.2528129","article-title":"Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks","volume":"35","author":"Dou","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"R22-3-20240829","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.rbmo.2015.07.006","article-title":"'By the work, one knows the workman': the practice and profession of the embryologist and its translation to quality in the embryology laboratory","volume":"31","author":"Go","year":"2015","journal-title":"Reprod Biomed Online"}],"container-title":["Asian Journal of Andrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/aja.aja_66_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T19:28:07Z","timestamp":1786735687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/aja.aja_66_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":16,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.4103\/aja.aja_66_20","relation":{},"ISSN":["1008-682X"],"issn-type":[{"value":"1008-682X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,3]]}}}