PMID- 38438694
OWN - NLM
STAT- Publisher
LR  - 20240304
IS  - 2948-2933 (Electronic)
IS  - 2948-2925 (Linking)
DP  - 2024 Mar 4
TI  - Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular 
      Carcinoma: A Meta-analysis.
LID - 10.1007/s10278-024-01058-1 [doi]
AB  - 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.
CI  - (c) 2024. The Author(s) under exclusive licence to Society for Imaging Informatics 
      in Medicine.
FAU - Salehi, Mohammad Amin
AU  - Salehi MA
AD  - School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
FAU - Harandi, Hamid
AU  - Harandi H
AD  - School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
AD  - Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam 
      Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
FAU - Mohammadi, Soheil
AU  - Mohammadi S
AD  - School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. 
      soheil.mhm@gmail.com.
FAU - Shahrabi Farahani, Mohammad
AU  - Shahrabi Farahani M
AD  - Medical Students Research Committee, Shahed University, Tehran, Iran.
FAU - Shojaei, Shayan
AU  - Shojaei S
AD  - School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
FAU - Saleh, Ramy R
AU  - Saleh RR
AD  - Department of Oncology, McGill University, Montreal, QC, H3A 0G4, Canada.
AD  - Division of Medical Oncology, McGill University Health Centre, Montreal, QC, H4A 
      3J1, Canada.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20240304
PL  - Switzerland
TA  - J Imaging Inform Med
JT  - Journal of imaging informatics in medicine
JID - 9918663679206676
SB  - IM
OTO - NOTNLM
OT  - Artificial intelligence
OT  - HCC
OT  - Hepatocellular carcinoma
OT  - Meta-analysis
EDAT- 2024/03/05 00:45
MHDA- 2024/03/05 00:45
CRDT- 2024/03/04 23:35
PHST- 2023/09/29 00:00 [received]
PHST- 2024/02/19 00:00 [accepted]
PHST- 2024/02/18 00:00 [revised]
PHST- 2024/03/05 00:45 [medline]
PHST- 2024/03/05 00:45 [pubmed]
PHST- 2024/03/04 23:35 [entrez]
AID - 10.1007/s10278-024-01058-1 [pii]
AID - 10.1007/s10278-024-01058-1 [doi]
PST - aheadofprint
SO  - J Imaging Inform Med. 2024 Mar 4. doi: 10.1007/s10278-024-01058-1.