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Italy segmentation from smartphone study
images and validating the
robustness of this approach
[52]
Mai et al. 2020 China L DL Establish and validate an artificial Retrospective Patient factors
neural network model to predict study
severe post-hepatectomy liver
failure in patients with
hepatocellular carcinoma who
underwent hemi-hepatectomy
Liu et al. [53] 2020 Taiwan L ML Devise a predictive model to Retrospective Patient factors
predict postoperative survival study
within 30 days based on the
patient’s preoperative
physiological measurement values
[54]
Schoenberg et al. 2020 Germany L ML Developing and validating a Retrospective Patient factors
machine-learning algorithm to study
predict which patients are
sufficiently treated by LR
Szpakowski et al. [55] 2020 USA G NLP Determine the growth pattern of Retrospective Radiology
GPs and their association with study reports
GBC
[56]
Capretti et al. 2021 Italy P CV/ML Develop a reliable and Retrospective CT
Portugal reproducible machine learning- study images/patient
based multimodal risk model factors
capable of predicting CR-POPF by
combining radiomic features and
morphologic features
[57]
Sun et al. 2021 China L DL Develop a model to predict HCC Retrospective Patient factors
recurrence study
Xie et al. [58] 2021 USA P NLP Develop and apply a natural Retrospective Radiology
language processing algorithm for study reports
the characterization of patients
diagnosed with chronic
pancreatitis
[59]
Hayashi et al. 2022 Japan P ML Predict recurrence and metastatic Retrospective Histology
sites in pancreatic cancer following study images
curative surgery
[60]
Li et al. 2022 China P ML Develop and validate clinical- Retrospective CT
radiomics models that study images/patient
preoperatively predict 1 and 2-year factors
recurrence of PDAC
[61]
Noh et al. 2022 South L ML Machine learning-based survival Retrospective Patient factors
Korea rate prediction of hepatocellular study
carcinoma patients
Morris-Stiff et al. [62] 2022 USA G NLP Develop a clinical prediction model Retrospective Radiology
for asymptomatic gallstones study reports
Narayan et al. [63] 2022 USA L ML/CV Developed an objective, computer Retrospective Histology
vision artificial intelligence (CVAI) study images
platform to score donor liver
steatosis and compared its
capability for predicting EAD
against pathologist steatosis
scores
Cotter et al. [64] 2022 USA G ML Machine-based learning approach Retrospective Patient factors
to stratify patients with gallbladder study
cancer into distinct prognostic
groups using preoperative
variables
CV: Computer vision; CR-POPF: clinically relevant postoperative pancreatic fistula; EAD: early allograft dysfunction; G: gallbladder; GPs:
gallbladder polyps; GBC: gallbladder cancer; L: liver; LR: liver resection; ML: machine learning; MVI: microvascular invasion; P: pancreas; PHLF:
post-hepatectomy liver failure; POPF: postoperative pancreatic fistula; PDAC: pancreatic ductal adenocarcinoma; RVI: radiogenomic venous
invasion.