Artificial intelligence assists operators in real-time detection of focal liver lesions during ultrasound: A randomized controlled study.

Autor: Tiyarattanachai T; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Apiparakoon T; Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Chaichuen O; Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Sukcharoen S; Division of Gastroenterology, Department of Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand., Yimsawad S; Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Jangsirikul S; Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Chaikajornwat J; Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Siriwong N; Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Burana C; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Siritaweechai N; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Atipas K; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Assawamasbunlue N; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Tovichayathamrong P; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Obcheuythed P; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Somvanapanich P; Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Geratikornsupuk N; Department of Medicine, Queen Savang Vadhana Memorial Hospital, The Thai Red Cross Society, Chonburi, Thailand., Anukulkarnkusol N; Gastroenterology and Liver Diseases Center, Mahachai Hospital, Samut Sakhon, Thailand., Sarakul P; Department of Radiology, Mahachai Hospital, Samut Sakhon, Thailand., Tanpowpong N; Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand., Pinjaroen N; Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Kerr SJ; Biostatistics Excellence Centre, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Rerknimitr R; Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Marukatat S; Image Processing and Understanding Team, Artificial Intelligence Research Group, National Electronics and Computer Technology Center, Thailand., Chaiteerakij R; Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. Electronic address: roon.chaiteerakij@chula.md.
Jazyk: angličtina
Zdroj: European journal of radiology [Eur J Radiol] 2023 Aug; Vol. 165, pp. 110932. Date of Electronic Publication: 2023 Jun 20.
DOI: 10.1016/j.ejrad.2023.110932
Abstrakt: Purpose: Detection of hepatocellular carcinoma (HCC) is crucial during surveillance by ultrasound. We previously developed an artificial intelligence (AI) system based on convolutional neural network for detection of focal liver lesions (FLLs) in ultrasound. The primary aim of this study was to evaluate whether the AI system can assist non-expert operators to detect FLLs in real-time, during ultrasound examinations.
Method: This single-center prospective randomized controlled study evaluated the AI system in assisting non-expert and expert operators. Patients with and without FLLs were enrolled and had ultrasound performed twice, with and without AI assistance. McNemar's test was used to compare paired FLL detection rates and false positives between groups with and without AI assistance.
Results: 260 patients with 271 FLLs and 244 patients with 240 FLLs were enrolled into the groups of non-expert and expert operators, respectively. In non-experts, FLL detection rate in the AI assistance group was significantly higher than the no AI assistance group (36.9 % vs 21.4 %, p < 0.001). In experts, FLL detection rates were not significantly different between the groups with and without AI assistance (66.7 % vs 63.3 %, p = 0.32). False positive detection rates in the groups with and without AI assistance were not significantly different in both non-experts (14.2 % vs 9.2 %, p = 0.08) and experts (8.6 % vs 9.0 %, p = 0.85).
Conclusions: The AI system resulted in significant increase in detection of FLLs during ultrasound examinations by non-experts. Our findings may support future use of the AI system in resource-limited settings where ultrasound examinations are performed by non-experts. The study protocol was registered under the Thai Clinical Trial Registry (TCTR20201230003), which is part of the WHO ICTRP Registry Network. The registry can be accessed via the following URL: https://trialsearch.who.int/Trial2.aspx?TrialID=TCTR20201230003.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier B.V.)
Databáze: MEDLINE
Externí odkaz: