{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T05:19:43Z","timestamp":1787548783476,"version":"build-2736575974"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,23]]},"DOI":"10.7759\/cureus.115047","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T04:39:15Z","timestamp":1787546355000},"source":"Crossref","is-referenced-by-count":0,"title":["Code-Free AutoML for Binary Classification of Fractured and Non-fractured Bone Radiographs From a Heterogeneous Public Dataset Using Google Cloud Vertex AI: A Proof-of-Concept Study"],"prefix":"10.7759","author":[{"given":"Mark A","family":"Bachir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neel","family":"Nawathey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Bachir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay J","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmeen K","family":"Cheema","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rakesh","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.injury.2006.04.130","article-title":"Epidemiology of adult fractures: a review","volume":"37","author":"Court-Brown CM","year":"2006","unstructured":"Court-Brown CM, Caesar B. Epidemiology of adult fractures: a review. Injury. 2006, 37:691-7. 10.1016\/j.injury.2006.04.130","journal-title":"Injury"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s13244-016-0534-1","article-title":"Error and discrepancy in radiology: inevitable or avoidable?","volume":"8","author":"Brady AP","year":"2017","unstructured":"Brady AP. Error and discrepancy in radiology: inevitable or avoidable?. Insights Imaging. 2017, 8:171-82. 10.1007\/s13244-016-0534-1","journal-title":"Insights Imaging"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211785","article-title":"Artificial intelligence in fracture detection: a systematic review and meta-analysis","volume":"304","author":"Kuo RY","year":"2022","unstructured":"Kuo RY, Harrison C, Curran TA, et al.. Artificial intelligence in fracture detection: a systematic review and meta-analysis. Radiology. 2022, 304:50-62. 10.1148\/radiol.211785","journal-title":"Radiology"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2024.04.009","article-title":"Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis","volume":"79","author":"Nowroozi A","year":"2024","unstructured":"Nowroozi A, Salehi MA, Shobeiri P, Agahi S, Momtazmanesh S, Kaviani P, Kalra MK. Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clin Radiol. 2024, 79:579-88. 10.1016\/j.crad.2024.04.009","journal-title":"Clin Radiol"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1806905115","article-title":"Deep neural network improves fracture detection by clinicians","volume":"115","author":"Lindsey R","year":"2018","unstructured":"Lindsey R, Daluiski A, Chopra S, et al.. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018, 115:11591-6. 10.1073\/pnas.1806905115","journal-title":"Proc Natl Acad Sci U S A"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/17453674.2017.1344459","article-title":"Artificial intelligence for analyzing orthopedic trauma radiographs","volume":"88","author":"Olczak J","year":"2017","unstructured":"Olczak J, Fahlberg N, Maki A, et al.. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017, 88:581-6. 10.1080\/17453674.2017.1344459","journal-title":"Acta Orthop"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2017.11.015","article-title":"Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks","volume":"73","author":"Kim DH","year":"2018","unstructured":"Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018, 73:439-45. 10.1016\/j.crad.2017.11.015","journal-title":"Clin Radiol"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.19.21117","article-title":"Artificial intelligence in musculoskeletal imaging: current status and future directions","volume":"213","author":"Gyftopoulos S","year":"2019","unstructured":"Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: current status and future directions. AJR Am J Roentgenol. 2019, 213:506-13. 10.2214\/AJR.19.21117","journal-title":"AJR Am J Roentgenol"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens G","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, et al.. A survey on deep learning in medical image analysis. Med Image Anal. 2017, 42:60-88. 10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva A","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, et al.. A guide to deep learning in healthcare. Nat Med. 2019, 25:24-9. 10.1038\/s41591-018-0316-z","journal-title":"Nat Med"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol EJ","year":"2019","unstructured":"Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019, 25:44-56. 10.1038\/s41591-018-0300-7","journal-title":"Nat Med"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002686","article-title":"Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists","volume":"15","author":"Rajpurkar P","year":"2018","unstructured":"Rajpurkar P, Irvin J, Ball RL, et al.. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018, 15:e1002686. 10.1371\/journal.pmed.1002686","journal-title":"PLoS Med"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(19)30108-6","article-title":"Automated deep learning design for medical image classification by healthcare professionals with no coding experience: a feasibility study","volume":"1","author":"Faes L","year":"2019","unstructured":"Faes L, Wagner SK, Fu DJ, et al.. Automated deep learning design for medical image classification by healthcare professionals with no coding experience: a feasibility study. Lancet Digit Health. 2019, 1:232-42. 10.1016\/S2589-7500(19)30108-6","journal-title":"Lancet Digit Health"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00305-2","article-title":"Code-free deep learning for multi-modality medical image classification","volume":"3","author":"Wagner SK","year":"2021","unstructured":"Wagner SK, Fu DJ, Faes L, et al.. Code-free deep learning for multi-modality medical image classification. Nat Mach Intell. 2021, 3:288-98. 10.1038\/s42256-021-00305-2","journal-title":"Nat Mach Intell"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-60429-4","article-title":"A comparative study of an on premise AutoML solution for medical image classification","volume":"14","author":"Elangovan K","year":"2024","unstructured":"Elangovan K, Lim G, Ting D. A comparative study of an on premise AutoML solution for medical image classification. Sci Rep. 2024, 14:10483. 10.1038\/s41598-024-60429-4","journal-title":"Sci Rep"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s12194-026-01077-3","article-title":"Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps","author":"Ragu N","year":"2026","unstructured":"Ragu N, Teo J. Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps. Radiol Phys Technol. 2026, 10.1007\/s12194-026-01077-3","journal-title":"Radiol Phys Technol"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers","volume":"2","author":"Mongan J","year":"2020","unstructured":"Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020, 2:e200029. 10.1148\/ryai.2020200029","journal-title":"Radiol Artif Intell"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins GS","year":"2024","unstructured":"Collins GS, Moons KG, Dhiman P, et al.. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024, 385:e078378. 10.1136\/bmj-2023-078378","journal-title":"BMJ"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683","article-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study","volume":"15","author":"Zech JR","year":"2018","unstructured":"Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 2018, 15:e1002683. 10.1371\/journal.pmed.1002683","journal-title":"PLoS Med"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany DS","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, et al.. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018, 172:1122-31. 10.1016\/j.cell.2018.02.010","journal-title":"Cell"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.17632\/cd6jjsxz44.3","article-title":"X-ray imaging dataset for detecting fractured vs non-fractured bones","volume":"3","author":"Hemamipour M","year":"2025","unstructured":"Hemamipour M. X-ray imaging dataset for detecting fractured vs non-fractured bones. Mendeley Data. 2025, 3:10.17632\/cd6jjsxz44.3","journal-title":"Mendeley Data"},{"key":"ref22","article-title":"Human bones fractured vs. non-fractured dataset","author":"Hemamipour M","year":"2025","unstructured":"Hemamipour M. Human bones fractured vs. non-fractured dataset. Kaggle. 2025, Accessed: August 1, 2026: https:\/\/www.kaggle.com\/datasets\/mohadesehemamipour\/human-bones-fractured-vs-non-fractured-dataset."},{"key":"ref23","unstructured":"Train an image classification model. (2026). Accessed. August 2, 2026: https:\/\/docs.cloud.google.com\/gemini-enterprise-agent-platform\/machine-learning\/image-data\/classification\/train-model."}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/525408-code-free-automl-for-binary-classification-of-fractured-and-non-fractured-bone-radiographs-from-a-heterogeneous-public-dataset-using-google-cloud-vertex-ai-a-proof-of-concept-study","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T04:39:26Z","timestamp":1787546366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/525408-code-free-automl-for-binary-classification-of-fractured-and-non-fractured-bone-radiographs-from-a-heterogeneous-public-dataset-using-google-cloud-vertex-ai-a-proof-of-concept-study"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,23]]},"references-count":23,"URL":"https:\/\/doi.org\/10.7759\/cureus.115047","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8,23]]}}}