Artificial intelligence for predicting survival following deceased donor liver transplantation: Retrospective multi-center study

Yu, Young-Dong; Lee, Kwang-Sig; Kim, Jong Man; Ryu, Je Ho; Lee, Jae-Geun; Lee, Kwang‐Woong; Kim, Bong-Wan; Kim, Joo Young

doi:10.1016/j.ijsu.2022.106838

Cited by 10 publications

(28 citation statements)

References 27 publications

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“…The importance of the artificial intelligence approach for outcomes prediction following liver transplantation was highlighted in a Korean registry involving 785 deceased donor transplant recipients [ 25 ]. Among all analyzed methods, the random forest model had the best predictive power for survival at one month, 3, and 12 months (respectively, AUC = 0.80, AUC = 0.85, and AUC = 0.81).…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience

Zabara

Popescu

Burlacu

et al. 2023

Sensors

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Background and Objectives: In the early period after liver transplantation, patients are exposed to a high rate of complications and several scores are currently available to predict adverse postoperative outcomes. However, an ideal, universally accepted and validated score to predict adverse events in liver transplant recipients with hepatitis C is lacking. Therefore, we aimed to establish and validate a machine learning (ML) model to predict short-term outcomes of hepatitis C patients who underwent liver transplantation. Materials and Methods: We conducted a retrospective observational two-center cohort study involving hepatitis C patients who underwent liver transplantation. Based on clinical and laboratory parameters, the dataset was used to train a deep-learning model for predicting short-term postoperative complications (within one month following liver transplantation). Adverse events prediction in the postoperative setting was the primary study outcome. Results: A total of 90 liver transplant recipients with hepatitis C were enrolled in the present study, 80 patients in the training cohort and ten in the validation cohort, respectively. The age range of the participants was 12–68 years, 51 (56,7%) were male, and 39 (43.3%) were female. Throughout the 85 training epochs, the model achieved a very good performance, with the accuracy ranging between 99.76% and 100%. After testing the model on the validation set, the deep-learning classifier confirmed the performance in predicting postoperative complications, achieving an accuracy of 100% on unseen data. Conclusions: We successfully developed a ML model to predict postoperative complications following liver transplantation in hepatitis C patients. The model demonstrated an excellent performance for accurate adverse event prediction. Consequently, the present study constitutes the foundation for careful and non-invasive identification of high-risk patients who might benefit from a more intensive postoperative monitoring strategy.

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Section: Discussionmentioning

confidence: 99%

“…Therefore, ML methods might be a better solution than classic models for outcomes prediction in patients with liver transplantation. Nevertheless, only 6.4% of patients ( n = 50) had hepatitis C, and subgroup analysis in this particular subset of patients was not performed [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience

Zabara

Popescu

Burlacu

et al. 2023

Sensors

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“…Conventional research covers a limited range of predictors for the early diagnosis of disease, using logistic regression with an unrealistic assumption of ceteris paribus , i.e., “all the other variables staying constant”. For this reason, emerging literature employs artificial intelligence for the early diagnosis of disease, e.g., arrhythmia [ 8 ], birth outcome [ 9 , 10 ], cancer [ 11 , 12 ], comorbidity [ 13 ], depression [ 14 ], liver transplantation [ 15 ], menopause [ 16 , 17 ], and temporomandibular disease [ 18 , 19 ]. It is free from unrealistic assumptions of “all the other variables staying constant”.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the notion of explainable artificial intelligence is enjoying immense popularity now. Explainable artificial intelligence can be defined as “artificial intelligence to identify major predictors of the dependent variable”, and there are four approaches of explainable artificial intelligence at this point, i.e., random forest impurity importance, random forest permutation importance [ 20 , 21 ], machine learning accuracy importance, and Shapley additive explanations (SHAP) [ 15 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. Random forest impurity importance calculates the node impurity decrease from the creation of a branch on a certain predictor.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning accuracy importance (an extension of random forest permutation importance) calculates the accuracy decrease from the exclusion of data on the predictor. The SHAP value of a predictor for a participant measures the difference between what machine learning predicts for the probability of GID with and without the predictor [ 15 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. For example, let us assume in a hypothetical figure ( Figure 1 ) that the SHAP values of diabetes (x033) for GERD have the range of (−0.05, 0.30).…”

Section: Introductionmentioning

confidence: 99%

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Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

Lee

Kim

2022

Diagnostics

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This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were “gastrointestinal” (title) together with “random forest” or ”explainable artificial intelligence” (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70–0.98 for accuracy, 0.04–0.25 for sensitivity, and 0.54–0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.

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Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Bianchi,

Giambusso,

De Iacob

et al. 2024

Updates Surg

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Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons’ ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

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Artificial intelligence for predicting survival following deceased donor liver transplantation: Retrospective multi-center study

Cited by 10 publications

References 27 publications

Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience

Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience

Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

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