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Computer-aided detection of equivocal spinal tuberculosis on X-ray using a YOLOv11-based deep learning model.

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Title: Computer-aided detection of equivocal spinal tuberculosis on X-ray using a YOLOv11-based deep learning model.
Authors: Yuan Y; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Ma J; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Ma H; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Zhang M; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Qiu X; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Shen L; Department of Deepwise AI Lab, Hangzhou Deepwise & League of PHD Technology Co., Ltd., Hangzhou, China., Ren Z; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Wang J; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Abulizi A; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Hu W; Department of Spine Surgery, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China., Nijiati M; Department of Radiology, Medical Imaging Center, Xinjiang Medical University Affiliated Fourth Hospital, Urumqi, China.; Xinjiang Key Laboratory of Artificial Intelligence Assisted Imaging Diagnosis, Kashi, China.; Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Source: Frontiers in public health [Front Public Health] 2026 Jun 10; Vol. 14, pp. 1780946. Date of Electronic Publication: 2026 Jun 10 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE
Database: MEDLINE Ultimate
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              Text: 2026 Jun 10
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