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. 2021 Jun 8;16(6):e0252882.
doi: 10.1371/journal.pone.0252882. eCollection 2021.

Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images

Thodsawit Tiyarattanachai  1 , Terapap Apiparakoon  2 , Sanparith Marukatat  3 , Sasima Sukcharoen  4 , Nopavut Geratikornsupuk  5 , Nopporn Anukulkarnkusol  6 , Parit Mekaroonkamol  4 , Natthaporn Tanpowpong  7 , Pamornmas Sarakul  8 , Rungsun Rerknimitr  9 , Roongruedee Chaiteerakij  9
Affiliations

Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images

Thodsawit Tiyarattanachai et al. PLoS One. .

Abstract

Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3-89.6), 83.9% (95%CI: 80.3-87.4), and 97.1% (95%CI: 96.5-97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7-78.3), 84.9% (95%CI: 81.6-88.2), and 97.1% (95%CI: 96.5-97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3-82.8), 97.8% (95%CI: 96.7-98.9), and 96.5% (95%CI: 95.0-97.9) on the internal test set; and 81.5% (95%CI: 74.2-88.8), 94.4% (95%CI: 92.8-96.0), and 97.4% (95%CI: 96.2-98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1
Example images of HCC (1a), cyst (1b), hemangioma (1c), FFS (1d), and FFI (1e) included in the study. Left panels show original images inputted into the AI system. Right panels show AI-outputted bounding boxes around each lesion along with the predicted diagnoses and its confidence value for prediction.
Fig 2
Fig 2. Process of AI system training and evaluation.
See this image and copyright information in PMC

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References

    1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2018;68(6):394–424. doi: 10.3322/caac.21492 - DOI - PubMed
    1. Chaiteerakij R, Chattieng P, Choi J, Pinchareon N, Thanapirom K, Geratikornsupuk N. Surveillance for Hepatocellular Carcinoma Reduces Mortality: an Inverse Probability of Treatment Weighted Analysis. Annals of hepatology. 2017;16(3):421–9. Epub 2017/04/21. doi: 10.5604/16652681.1235485 . - DOI - PubMed
    1. Zhang BH, Yang BH, Tang ZY. Randomized controlled trial of screening for hepatocellular carcinoma. Journal of cancer research and clinical oncology. 2004;130(7):417–22. Epub 2004/03/26. doi: 10.1007/s00432-004-0552-0 . - DOI - PubMed
    1. Singal AG, Pillai A, Tiro J. Early detection, curative treatment, and survival rates for hepatocellular carcinoma surveillance in patients with cirrhosis: a meta-analysis. PLoS medicine. 2014;11(4):e1001624. Epub 2014/04/03. doi: 10.1371/journal.pmed.1001624 ; PubMed Central PMCID: PMC3972088. - DOI - PMC - PubMed
    1. Yang JD, Mannalithara A, Piscitello AJ, Kisiel JB, Gores GJ, Roberts LR, et al.. Impact of surveillance for hepatocellular carcinoma on survival in patients with compensated cirrhosis. Hepatology (Baltimore, Md). 2018;68(1):78–88. Epub 2017/10/13. doi: 10.1002/hep.29594 ; PubMed Central PMCID: PMC5897179. - DOI - PMC - PubMed

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Grants and funding

This research project is supported by The Second Century Fund (C2F), Chulalongkorn University (TT); Ratchadapisek Sompoch Endowment Fund (2019) under Telehealth Cluster, Chulalongkorn University (RC); and Grant for International Research Integration: Chula Research Scholar, Ratchadaphiseksomphot Endowment Fund, Chulalongkorn University (RR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.