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Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis

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Abstract

Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians’ performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.

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Data Availability

The data that support the findings of this study are available from the authors upon reasonable request.

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Authors and Affiliations

  1. School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Mohammad Amin Salehi, Hamid Harandi, Soheil Mohammadi & Shayan Shojaei

  2. Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran

    Hamid Harandi

  3. Medical Students Research Committee, Shahed University, Tehran, Iran

    Mohammad Shahrabi Farahani

  4. Department of Oncology, McGill University, Montreal, QC, H3A 0G4, Canada

    Ramy R. Saleh

  5. Division of Medical Oncology, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada

    Ramy R. Saleh

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Salehi, M.A., Harandi, H., Mohammadi, S. et al. Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01058-1

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