A multicenter clinical AI system study for detection and diagnosis of focal liver lesions
© 2024. The Author(s)..
Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Nature communications - 15(2024), 1 vom: 07. Feb., Seite 1131 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ying, Hanning [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 09.02.2024 Date Revised 24.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41467-024-45325-9 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368155846 |
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| 245 | 1 | 2 | |a A multicenter clinical AI system study for detection and diagnosis of focal liver lesions |
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| 520 | |a Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions | ||
| 650 | 4 | |a Multicenter Study | |
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| 700 | 1 | |a Zhang, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Yiyue |e verfasserin |4 aut | |
| 700 | 1 | |a Zhen, Shihui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaojie |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Duan, Lian |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Mingzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Xiangdong |e verfasserin |4 aut | |
| 700 | 1 | |a Gong, Xiangyang |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Haitao |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Jianshuai |e verfasserin |4 aut | |
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