{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:08:07Z","timestamp":1787670487035,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T00:00:00Z","timestamp":1734739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective and dependent on the skill and experience of the examiner. In recent years, artificial intelligence (AI)-based techniques have emerged as a promising tool in medical imaging, offering the potential to revolutionize tissue phenotyping and improve the accuracy and reliability of the histopathological examination process. The goal of this study was to investigate the use of AI techniques for the detection of various tissue phenotypes in POC after spontaneous abortion and evaluate the accuracy and reliability of these techniques compared to traditional manual methods. Methods: We present a novel publicly available dataset named HistoPoC, which is believed to be the first of its kind, focusing on spontaneous abortion (miscarriage) in early pregnancy. A diverse dataset of 5666 annotated images was prepared from previously diagnosed cases of POC from Atia General Hospital, Karachi, Pakistan, for this purpose. The digital images were prepared at 10\u00d7 through a camera-connected microscope by a consultant histopathologist. Results: The dataset\u2019s effectiveness was validated using several deep learning-based models, demonstrating its applicability and supporting its use in intelligent diagnostic systems. Conclusions: The insights gained from this study could illuminate the causes of spontaneous abortion and guide the development of novel treatments. Additionally, this study could contribute to advancements in the field of tissue phenotyping and the wider application of deep learning techniques in medical diagnostics and treatment.<\/jats:p>","DOI":"10.3390\/diagnostics14242877","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T05:58:27Z","timestamp":1734933507000},"page":"2877","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Computer-Aided Diagnosis in Spontaneous Abortion: A Histopathology Dataset and Benchmark for Products of Conception"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-9532","authenticated-orcid":false,"given":"Tahir","family":"Mahmood","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeeshan","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Neurophysiology, Cork University Hospital, T12 DC4A Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atif","family":"Latif","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul 04620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7389-6493","authenticated-orcid":false,"given":"Binish Arif","family":"Sultan","sequence":"additional","affiliation":[{"name":"Department of Pathology, Jinnah Sindh Medical University, Karachi 75510, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2694-9364","authenticated-orcid":false,"given":"Muhammad","family":"Zubair","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0184-7620","authenticated-orcid":false,"given":"Zahid","family":"Ullah","sequence":"additional","affiliation":[{"name":"Division of AI Software Convergence, Dongguk University, Seoul 04620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"AbuZar","family":"Ansari","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Ewha Woman\u2019s University, Seoul 07985, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talat","family":"Zehra","sequence":"additional","affiliation":[{"name":"Department of Pathology, Jinnah Sindh Medical University, Karachi 75510, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-2956","authenticated-orcid":false,"given":"Shahzad","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Hanyang University, Seoul 04763, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naqqash","family":"Dilshad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1097\/GRF.0b013e318253b192","article-title":"Work and Work-Related Stress in Pregnancy","volume":"55","author":"Katz","year":"2012","journal-title":"Clin. 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