The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videos

Despite the wide availability of ultrasound machines for hepatocellular carcinoma surveillance, an inadequate number of expert radiologists performing ultrasounds in remote areas remains a primary barrier for surveillance. We demonstrated feasibility of artificial intelligence (AI) to aid in the det...

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Bibliographic Details
Published in:Scientific reports 2022-05, Vol.12 (1), p.7749-7749, Article 7749
Main Authors: Tiyarattanachai, Thodsawit, Apiparakoon, Terapap, Marukatat, Sanparith, Sukcharoen, Sasima, Yimsawad, Sirinda, Chaichuen, Oracha, Bhumiwat, Siwat, Tanpowpong, Natthaporn, Pinjaroen, Nutcha, Rerknimitr, Rungsun, Chaiteerakij, Roongruedee
Format: Article
Language:English
Subjects:
639/166/985
692/4020/4021/1607/1610/4029
692/700/1421/1860
Artificial Intelligence
Feasibility Studies
Hepatocellular carcinoma
Humanities and Social Sciences
Humans
Lesions
Liver
Liver cancer
Liver Neoplasms - diagnostic imaging
multidisciplinary
Radiologists
Science
Science (multidisciplinary)
Surveillance
Training
Ultrasonic imaging
Ultrasonography
Ultrasound
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Summary:Despite the wide availability of ultrasound machines for hepatocellular carcinoma surveillance, an inadequate number of expert radiologists performing ultrasounds in remote areas remains a primary barrier for surveillance. We demonstrated feasibility of artificial intelligence (AI) to aid in the detection of focal liver lesions (FLLs) during ultrasound. An AI system for FLL detection in ultrasound videos was developed. Data in this study were prospectively collected at a university hospital. We applied a two-step training strategy for developing the AI system by using a large collection of ultrasound snapshot images and frames from full-length ultrasound videos. Detection performance of the AI system was evaluated and then compared to detection performance by 25 physicians including 16 non-radiologist physicians and 9 radiologists. Our dataset contained 446 videos (273 videos with 387 FLLs and 173 videos without FLLs) from 334 patients. The videos yielded 172,035 frames with FLLs and 1,427,595 frames without FLLs for training on the AI system. The AI system achieved an overall detection rate of 89.8% (95%CI: 84.5–95.0) which was significantly higher than that achieved by non-radiologist physicians (29.1%, 95%CI: 21.2–37.0, p  
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-11506-z