{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:02:52Z","timestamp":1790208172192,"version":"4.1.0"},"reference-count":54,"publisher":"SPIE-Intl Soc Optical Eng","issue":"02","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2025,3,18]]},"abstract":"<jats:p>PurposeIdentifying pregnant patients at high risk of hysterectomy before giving birth informs clinical management and improves outcomes. We aim to develop machine learning models to predict hysterectomy in pregnant women with placenta accreta spectrum (PAS).ApproachWe developed five machine learning models using information from magnetic resonance images and combined them with topographic maps and radiomic features to predict hysterectomy. The models were trained, optimized, and evaluated on data from 241 patients, in groups of 157, 24, and 60 for training, validation, and testing, respectively.ResultsWe assessed the models individually as well as using an ensemble approach. When these models are combined, the ensembled model produced the best performance and achieved an area under the curve of 0.90, a sensitivity of 90.0%, and a specificity of 90.0% for predicting hysterectomy.ConclusionsVarious machine learning models were developed to predict hysterectomy in pregnant women with PAS, which may have potential clinical applications to help improve patient management.<\/jats:p>","DOI":"10.1117\/1.jmi.12.2.024502","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T07:01:52Z","timestamp":1742281312000},"page":"1-13","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble of fine-tuned machine learning models for hysterectomy prediction in pregnant women using magnetic resonance images"],"prefix":"10.1117","volume":"12","author":[{"given":"Vishnu Vardhan Reddy Kanamata","family":"Reddy","sequence":"first","affiliation":[{"name":"The University of Texas at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Villordon","sequence":"additional","affiliation":[{"name":"The University of Texas at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9511-7866","authenticated-orcid":false,"given":"Quyen N.","family":"Do","sequence":"additional","affiliation":[{"name":"The University of Texas Southwestern Medical Center at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9743-3010","authenticated-orcid":false,"given":"Yin","family":"Xi","sequence":"additional","affiliation":[{"name":"The University of Texas Southwestern Medical Center at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew A.","family":"Lewis","sequence":"additional","affiliation":[{"name":"The University of Texas Southwestern Medical Center at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina L.","family":"Herrera","sequence":"additional","affiliation":[{"name":"The University of Texas Southwestern Medical Center at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5724-0342","authenticated-orcid":false,"given":"David","family":"Owen","sequence":"additional","affiliation":[{"name":"The University of Texas Southwestern Medical Center at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine Y.","family":"Spong","sequence":"additional","affiliation":[{"name":"The University of Texas Southwestern Medical Center at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diane M.","family":"Twickler","sequence":"additional","affiliation":[{"name":"The University of Texas Southwestern Medical Center at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9123-9484","authenticated-orcid":false,"given":"Baowei","family":"Fei","sequence":"additional","affiliation":[{"name":"The University of Texas at Dallas"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","reference":[{"key":"r1","first-page":"106","article-title":"Unusual shapes of the placenta: placenta accreta","author":"Benirschke","year":"1967"},{"key":"r2","doi-asserted-by":"publisher","DOI":"10.1016\/S0730-725X(99)00035-1"},{"key":"r3","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpobgyn.2020.06.007"},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2004.12.074"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.3109\/14767058.2015.1034103"},{"key":"r6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2010.09.013"},{"key":"r7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2007.03.026"},{"key":"r8","doi-asserted-by":"publisher","DOI":"10.4297\/najms.2011.358"},{"key":"r9","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000007930"},{"key":"r10","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0b013e3181c4f12a"},{"key":"r11","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0b013e3182051db2"},{"key":"r12","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-020-2790-9"},{"key":"r13","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00221"},{"key":"r15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"r16","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3049452"},{"key":"r17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002699"},{"key":"r18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"r19","first-page":"565","article-title":"V-Net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"r20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2018.03.001"},{"key":"r21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"r22","article-title":"An image is worth 16 \u00d7 16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"r23","doi-asserted-by":"publisher","DOI":"10.1145\/3505244"},{"key":"r24","first-page":"6000","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"r25","first-page":"4171","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"r26","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2021"},{"key":"r27","article-title":"A battle of network structures: an empirical study of CNN, transformer, and MLP","author":"Zhao","year":"2021"},{"key":"r28","first-page":"30392","article-title":"Early convolutions help transformers see better","author":"Xiao","year":"2021"},{"key":"r29","first-page":"3965","article-title":"CoAtNet: marrying convolution and attention for all data sizes","author":"Dai","year":"2021"},{"key":"r30","article-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","author":"Simpson","year":"2019"},{"key":"r31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"r32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693"},{"key":"r33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00872"},{"key":"r34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02006"},{"key":"r35","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"r36","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/13\/R150"},{"key":"r37","doi-asserted-by":"publisher","DOI":"10.1117\/12.2581467"},{"key":"r38","doi-asserted-by":"publisher","DOI":"10.1117\/12.2549873"},{"key":"r39","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.8.5.054001"},{"key":"r40","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2021310"},{"key":"r41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211060"},{"key":"r42","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26883"},{"key":"r43","doi-asserted-by":"publisher","DOI":"10.1117\/12.2611587"},{"key":"r44","doi-asserted-by":"publisher","DOI":"10.1117\/12.2611580"},{"key":"r45","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2205687"},{"key":"r46","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01342-5"},{"key":"r47","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2635663"},{"key":"r48","doi-asserted-by":"publisher","DOI":"10.1117\/12.2653663"},{"key":"r49","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","author":"Fabian","year":"2011"},{"key":"r50","first-page":"3149","article-title":"LightGBM: a highly efficient gradient boosting decision tree","author":"Ke","year":"2017"},{"key":"r51","first-page":"972","article-title":"Self-normalizing neural networks","author":"Klambauer","year":"2017"},{"key":"r52","first-page":"26","article-title":"Stratified cross-validation on multiple columns","author":"Motl","year":"2021"},{"key":"r53","article-title":"Gaussian error linear units (GELUs)","author":"Hendrycks","year":"2016"},{"key":"r54","article-title":"ReLU deep neural networks and linear finite elements","author":"He","year":"2018"}],"container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-12\/issue-02\/024502\/Ensemble-of-fine-tuned-machine-learning-models-for-hysterectomy-prediction\/10.1117\/1.JMI.12.2.024502.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T23:21:55Z","timestamp":1790205715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-12\/issue-02\/024502\/Ensemble-of-fine-tuned-machine-learning-models-for-hysterectomy-prediction\/10.1117\/1.JMI.12.2.024502.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":54,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2025,3,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.12.2.024502","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,18]]}}}