{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:32:57Z","timestamp":1790422377200,"version":"4.1.0"},"reference-count":18,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The assessment of osteonecrosis of the femoral head (ONFH) often presents challenges in accuracy and efficiency. Traditional methods rely on imaging studies and clinical judgment, prompting the need for advanced approaches. This study aims to use deep learning algorithms to enhance disease assessment and prediction in ONFH, optimizing treatment strategies.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The primary objective of this research is to analyze pathological images of ONFH using advanced deep learning algorithms to evaluate treatment response, vascular reconstruction, and disease progression. By identifying the most effective algorithm, this study seeks to equip clinicians with precise tools for disease assessment and prediction.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Magnetic resonance imaging (MRI) data from 30 patients diagnosed with ONFH were collected, totaling 1200 slices, which included 675 slices with lesions and 225 normal slices. The dataset was divided into training (630 slices), validation (135 slices), and test (135 slices) sets. A total of 10 deep learning algorithms were tested for training and optimization, and MobileNetV3_Large was identified as the optimal model for subsequent analyses. This model was applied for quantifying vascular reconstruction, evaluating treatment responses, and assessing lesion progression. In addition, a long short-term memory (LSTM) model was integrated for the dynamic prediction of time-series data.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The MobileNetV3_Large model demonstrated an accuracy of 96.5% (95% CI 95.1%\u201097.8%) and a recall of 94.8% (95% CI 93.2%\u201096.4%) in ONFH diagnosis, significantly outperforming DenseNet201 (87.3%;\n P<\/jats:italic>\n <.05). Quantitative evaluation of treatment responses showed that vascularized bone grafting resulted in an average increase of 12.4 mm in vascular length (95% CI 11.2\u201013.6 mm;\n P<\/jats:italic>\n <.01) and an increase of 2.7 in branch count (95% CI 2.3\u20103.1;\n P<\/jats:italic>\n <.01) among the 30 patients. The model achieved an AUC of 0.92 (95% CI 0.90\u20100.94) for predicting lesion progression, outperforming traditional methods like ResNet50 (AUC=0.85;\n P<\/jats:italic>\n <.01). Predictions were consistent with clinical observations in 92.5% of cases (24\/26).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The application of deep learning algorithms in examining treatment response, vascular reconstruction, and disease progression in ONFH presents notable advantages. This study offers clinicians a precise tool for disease assessment and highlights the significance of using advanced technological solutions in health care practice.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/66727","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T19:11:06Z","timestamp":1754507466000},"page":"e66727-e66727","source":"Crossref","is-referenced-by-count":2,"title":["Predictive Modeling of Osteonecrosis of the Femoral Head Progression Using MobileNetV3_Large and Long Short-Term Memory Network: Novel Approach"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9906-3370","authenticated-orcid":false,"given":"Gang","family":"Kong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7513-9805","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5732-4057","authenticated-orcid":false,"given":"Dan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5960-0607","authenticated-orcid":false,"given":"Jingbo","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3933-6684","authenticated-orcid":false,"given":"Kegui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","DOI":"10.21037\/atm-23-303","article-title":"Worldwide research trends on femur head necrosis (2000-2021): a bibliometrics analysis and suggestions for researchers","volume":"11","author":"Zhang","journal-title":"Ann Transl Med"},{"issue":"7","key":"R2","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1007\/s00264-018-3956-1","article-title":"Animal models of steroid-induced osteonecrosis of the femoral head-a comprehensive research review up to 2018","volume":"42","author":"Xu","journal-title":"Int Orthop"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.1037190","article-title":"Emerging roles of growth factors in osteonecrosis of the femoral head","volume":"13","author":"Che","journal-title":"Front Genet"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1186\/s12891-019-2778-7","article-title":"Correlational analysis between neutrophil granulocyte levels and osteonecrosis of the femoral head","volume":"20","author":"Jiang","journal-title":"BMC Musculoskelet Disord"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0300060514554724","article-title":"Biomarkers and pharmaceutical strategies in steroid-induced osteonecrosis of the femoral head: a literature review","volume":"43","author":"Qi","journal-title":"J Int Med Res"},{"issue":"1","key":"R6","doi-asserted-by":"publisher","first-page":"690","DOI":"10.54112\/bcsrj.v2024i1.690","article-title":"Diagnostic accuracy of X-ray in diagnosing avascular femoral head, keeping magnetic resonance imaging (MRI) as gold standard","volume":"2024","author":"Shakir","journal-title":"Biol Clin Sci Res J"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","DOI":"10.1186\/s13018-018-0836-8","article-title":"Accuracy of MRI diagnosis of early osteonecrosis of the femoral head: a meta-analysis and systematic review","volume":"13","author":"Zhang","journal-title":"J Orthop Surg Res"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2019.115099","article-title":"T1-weighted MRI images accurately represent the volume and surface of architectural mineral damage of osteonecrosis of the femoral head: Comparison with high-resolution computed tomography","volume":"130","author":"Pascart","journal-title":"Bone"},{"issue":"4","key":"R9","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.2214\/AJR.20.22930","article-title":"Comparison of radiographic and pathologic diagnosis of osteonecrosis of the femoral head","volume":"216","author":"Crim","journal-title":"AJR Am J Roentgenol"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.573522","article-title":"Deep learning improves osteonecrosis prediction of femoral head after internal fixation using hybrid patient and radiograph variables","volume":"7","author":"Zhu","journal-title":"Front Med (Lausanne)"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","first-page":"36675814","DOI":"10.3390\/jpm13010153","article-title":"A deep learning method for quantification of femoral head necrosis based on routine hip MRI for improved surgical decision making","volume":"13","author":"Ruckli","journal-title":"J Pers Med"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2024.1471692","article-title":"A single sequence MRI-based deep learning radiomics model in the diagnosis of early osteonecrosis of femoral head","volume":"12","author":"Alkhatatbeh","journal-title":"Front Bioeng Biotechnol"},{"issue":"22","key":"R13","doi-asserted-by":"publisher","first-page":"4652","DOI":"10.3390\/electronics12224652","article-title":"Enhancing electrical load prediction using a bidirectional LSTM neural network","volume":"12","author":"Pavlatos","journal-title":"Electronics (Basel)"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","DOI":"10.1186\/s12886-021-02062-7","article-title":"Suprachoroidal hemorrhage associated with pars plana vitrectomy","volume":"21","author":"Mo","journal-title":"BMC Ophthalmol"},{"issue":"1","key":"R15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: a survey","volume":"15","author":"Sarvamangala","journal-title":"Evol Intell"},{"issue":"4","key":"R16","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1016\/j.icte.2024.05.007","article-title":"Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: methods, taxonomy, challenges and prospects","volume":"10","author":"Sarker","journal-title":"ICT Express"},{"issue":"2","key":"R17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/99608f92.5a8a3a3d","article-title":"Why are we using black box models in AI when we don\u2019t need to? A lesson from an explainable AI competition","volume":"1","author":"Rudin","journal-title":"Harvard Data Science Review"},{"key":"R18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105111","article-title":"Transparency of deep neural networks for medical image analysis: a review of interpretability methods","volume":"140","author":"Salahuddin","journal-title":"Comput Biol Med"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2025\/1\/e66727","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:28:32Z","timestamp":1785536912000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2025\/1\/e66727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":18,"URL":"https:\/\/doi.org\/10.2196\/66727","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,6]]},"article-number":"v13i3e66727"}}