AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening
Abstract Hepatocellular carcinoma (HCC) ultrasound screening encounters challenges related to accuracy and the workload of radiologists. This retrospective, multicenter study assessed four artificial intelligence (AI) enhanced strategies using 21,934 liver ultrasound images from 11,960 patients to i...
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Nature Portfolio
2025-08-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01892-9 |
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| author | Rui-Fang Lu Chao-Yin She Dan-Ni He Mei-Qing Cheng Ying Wang Hui Huang Ya-Dan Lin Jia-Yi Lv Si Qin Ze-Zhi Liu Zhi-Rong Lu Wei-Ping Ke Chao-Qun Li Han Xiao Zuo-Feng Xu Guang-Jian Liu Hong Yang Jie Ren Hai-Bo Wang Ming-De Lu Qing-Hua Huang Li-Da Chen Wei Wang Ming Kuang |
| author_facet | Rui-Fang Lu Chao-Yin She Dan-Ni He Mei-Qing Cheng Ying Wang Hui Huang Ya-Dan Lin Jia-Yi Lv Si Qin Ze-Zhi Liu Zhi-Rong Lu Wei-Ping Ke Chao-Qun Li Han Xiao Zuo-Feng Xu Guang-Jian Liu Hong Yang Jie Ren Hai-Bo Wang Ming-De Lu Qing-Hua Huang Li-Da Chen Wei Wang Ming Kuang |
| author_sort | Rui-Fang Lu |
| collection | DOAJ |
| description | Abstract Hepatocellular carcinoma (HCC) ultrasound screening encounters challenges related to accuracy and the workload of radiologists. This retrospective, multicenter study assessed four artificial intelligence (AI) enhanced strategies using 21,934 liver ultrasound images from 11,960 patients to improve HCC ultrasound screening accuracy and reduce radiologist workload. UniMatch was used for lesion detection and LivNet for classification, trained on 17,913 images. Among the strategies tested, Strategy 4, which combined AI for initial detection and radiologist evaluation of negative cases in both detection and classification phases, outperformed others. It not only matched the high sensitivity of original algorithm (0.956 vs. 0.991) but also improved specificity (0.787 vs. 0.698), reduced radiologist workload by 54.5%, and decreased both recall and false positive rates. This approach demonstrates a successful model of human-AI collaboration, not only enhancing clinical outcomes but also mitigating unnecessary patient anxiety and system burden by minimizing recalls and false positives. |
| format | Article |
| id | doaj-art-002a3a72d22c4a7482c2002f1b97c612 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-002a3a72d22c4a7482c2002f1b97c6122025-08-20T03:06:08ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111210.1038/s41746-025-01892-9AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screeningRui-Fang Lu0Chao-Yin She1Dan-Ni He2Mei-Qing Cheng3Ying Wang4Hui Huang5Ya-Dan Lin6Jia-Yi Lv7Si Qin8Ze-Zhi Liu9Zhi-Rong Lu10Wei-Ping Ke11Chao-Qun Li12Han Xiao13Zuo-Feng Xu14Guang-Jian Liu15Hong Yang16Jie Ren17Hai-Bo Wang18Ming-De Lu19Qing-Hua Huang20Li-Da Chen21Wei Wang22Ming Kuang23Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversitySchool of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical UniversityDepartment of Medical Ultrasonics, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangzhou Medical universityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen UniversityDepartment of Medical Ultrasonics, Sanshui District People’s HospitalDepartment of Medical Ultrasonics, Sanshui District People’s HospitalDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityDepartment of Medical Ultrasound, West China Xiamen Hospital of Sichuan UniversityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityDepartment of Medical Ultrasonics, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasonics, The Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversitySchool of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical UniversityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen UniversityAbstract Hepatocellular carcinoma (HCC) ultrasound screening encounters challenges related to accuracy and the workload of radiologists. This retrospective, multicenter study assessed four artificial intelligence (AI) enhanced strategies using 21,934 liver ultrasound images from 11,960 patients to improve HCC ultrasound screening accuracy and reduce radiologist workload. UniMatch was used for lesion detection and LivNet for classification, trained on 17,913 images. Among the strategies tested, Strategy 4, which combined AI for initial detection and radiologist evaluation of negative cases in both detection and classification phases, outperformed others. It not only matched the high sensitivity of original algorithm (0.956 vs. 0.991) but also improved specificity (0.787 vs. 0.698), reduced radiologist workload by 54.5%, and decreased both recall and false positive rates. This approach demonstrates a successful model of human-AI collaboration, not only enhancing clinical outcomes but also mitigating unnecessary patient anxiety and system burden by minimizing recalls and false positives.https://doi.org/10.1038/s41746-025-01892-9 |
| spellingShingle | Rui-Fang Lu Chao-Yin She Dan-Ni He Mei-Qing Cheng Ying Wang Hui Huang Ya-Dan Lin Jia-Yi Lv Si Qin Ze-Zhi Liu Zhi-Rong Lu Wei-Ping Ke Chao-Qun Li Han Xiao Zuo-Feng Xu Guang-Jian Liu Hong Yang Jie Ren Hai-Bo Wang Ming-De Lu Qing-Hua Huang Li-Da Chen Wei Wang Ming Kuang AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening npj Digital Medicine |
| title | AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening |
| title_full | AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening |
| title_fullStr | AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening |
| title_full_unstemmed | AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening |
| title_short | AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening |
| title_sort | ai enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening |
| url | https://doi.org/10.1038/s41746-025-01892-9 |
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