Academic Journal
Artificial Intelligence-Assisted Liver Ultrasound Training: Image Recognition and Real-Time Feedback System Based on YOLO and DeepSeek.
| Τίτλος: | Artificial Intelligence-Assisted Liver Ultrasound Training: Image Recognition and Real-Time Feedback System Based on YOLO and DeepSeek. |
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| Συγγραφείς: | Liu T; Department of Ultrasonography, The Fifth People's Hospital of Chengdu, Chengdu, China (T.L., Q.W., Y.W.)., Wen Q; Department of Ultrasonography, The Fifth People's Hospital of Chengdu, Chengdu, China (T.L., Q.W., Y.W.)., Yang J; Operating Room, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China (J.Y.)., Luo Z; Glory Wireless Co. Ltd., Chengdu, China (Z.L.)., Wen Y; Department of Ultrasonography, The Fifth People's Hospital of Chengdu, Chengdu, China (T.L., Q.W., Y.W.). Electronic address: 13882211430@163.com. |
| Πηγή: | Academic radiology [Acad Radiol] 2026 Oct; Vol. 33 (10), pp. 4169-4178. Date of Electronic Publication: 2026 Aug 07. |
| Τύπος έκδοσης: | Journal Article; Randomized Controlled Trial |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Reston Va : Association Of University Radiologists Original Publication: Reston, VA : Association of University Radiologists, c1994- |
| Ιατρικοί όροι (MeSH): | Computer-Assisted Instruction*/methods , Liver*/diagnostic imaging , Radiology*/education , Artificial Intelligence*, Ultrasonography/methods ; Humans ; Detection Algorithms ; Large Language Models |
| Περίληψη: | Rationale and Objectives: This study aimed to develop and evaluate an artificial intelligence‑assisted liver ultrasound training system that combines the YOLOv11n object detection algorithm and the DeepSeek large language model for ultrasound education. Materials and Methods: A parallel controlled study was performed with 20 students randomly assigned to either the traditional instruction group (n = 10) or the AI‑assisted training group (n = 10). The YOLOv11n model was trained using 326 liver ultrasound images. Model performance metrics including precision, recall, mAP@0.5 and mAP@0.5:0.95 were calculated. System efficiency indicators, section recognition accuracy, evaluation consistency and post‑training examination scores were compared between groups. Grad‑CAM++ heatmaps were applied to visualize the model's attention on anatomical features. Results: The YOLOv11n model yielded a precision of 0.984, a recall of 1.00, an mAP@0.5 of 0.995, and an mAP@0.5:0.95 of 0.7099. Compared with traditional training, the AI‑assisted system reduced daily report processing time by 96.3% (from 486 to 18 min) and decreased feedback latency by 95.3% (from 44.8 to 2.1 min). Section recognition accuracy increased from 86.0% to 96.0%, evaluation consistency rose from 90.0% to 99.6%, and post‑training examination scores improved from 87.9 to 96.2 points. Grad‑CAM++ heatmaps verified that the model focused on anatomically meaningful regions. Conclusion: Integration of computer vision and large language models can improve teaching efficiency and learning performance in liver ultrasound training. (Copyright © 2026. Published by Elsevier Inc.) |
| Competing Interests: | Declaration of Competing Interest All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare. |
| Contributed Indexing: | Keywords: Artificial intelligence; DeepSeek; Liver ultrasound teaching; Teaching efficiency; YOLOv11n |
| Entry Date(s): | Date Created: 20260806 Date Completed: 20260929 Latest Revision: 20260930 |
| Update Code: | 20261001 |
| DOI: | 10.1016/j.acra.2026.06.070 |
| PMID: | 42562716 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1878-4046 |
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| DOI: | 10.1016/j.acra.2026.06.070 |