Artificial intelligence, machine learning, and deep learning in liver transplantation

A Machine Learning-Based Mortality Prediction Model for Patients with Chronic Hepatitis C Infection: An Exploratory Study

Background: Chronic hepatitis C (HCV) infection presents global health challenges with significant morbidity and mortality implications. Successfully treating patients with cirrhosis may lead to mortality rates comparable to the general population. This study aims to utilize machine learning techniques to create predictive mortality models for individuals with chronic HCV infections. Methods: Data from chronic HCV patients at Sultan Qaboos University Hospital (2009-2017) underwent analysis. Data pre-processing handled missing values and scaled features using Python via Anaconda. Model training involved SelectKBest feature selection and algorithms such as logistic regression, random forest, gradient boosting, and SVM. The evaluation included diverse metrics, with 5-fold cross-validation, ensuring consistent performance assessment. Results: A cohort of 702 patients meeting the eligibility criteria, predominantly male, with a median age of 47, was analyzed across a follow-up period of 97.4 months. Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively. Ten key features selected for mortality prediction included hemoglobin levels, alanine aminotransferase, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response. Machine learning models, including the logistic regression, random forest, gradient boosting, and support vector machine models, showed high discriminatory power, with logistic regression consistently achieving an AUC value of 0.929. Factors associated with increased mortality risk included cardiovascular diseases, coinfections, and failure to achieve a SVR, while lower ALT levels and specific HCV genotypes were linked to better survival outcomes. Conclusions: This study presents the use of machine learning models to predict mortality in chronic HCV patients, providing crucial insights for risk assessment and tailored treatments. Further validation and refinement of these models are essential to enhance their clinical utility, optimize patient care, and improve outcomes for individuals with chronic HCV infections.

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

BACKGROUND

Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout.

METHODS

Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005-2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model.

RESULTS

An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798-0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model.

CONCLUSION

The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.

Deciphering cuproptosis-related signatures in pediatric allergic asthma using integrated scRNA-seq and bulk RNA-seq analysis

OBJECTIVE

Allergic asthma (AA) is common in children. Excess copper is observed in AA patients. It is currently unclear whether copper imbalance can cause cuproptosis in pediatric AA.

METHODS

The datasets about pediatric AA (GSE40732 and GSE40888) were obtained from Gene Expression Omnibus (GEO) database. The expression of cuproptosis-related genes (CRGs) and immune cell infiltration in pediatric AA samples were analyzed. Single-cell RNA sequencing (scRNA-seq) data (GSE193816) were used to evaluate the expression patterns of CRGs in AA. The identification of differentially expressed genes within clusters was conducted using weighted gene co-expression network analysis. Subsequently, disease progression and cuproptosis-related models were screened using random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and general linear model (GLM) algorithms.

RESULTS

Four CRGs were notably increased in pediatric AA samples. CD4+ T cells, macrophages and mast cells exhibited a lower cuproptosis score in AA samples, indicating that these immune cells may be closely associated with cuproptosis in AA development. Co-expression network of CRGs in AA was constructed. AA samples were divided into two cuprotosis clusters. Following construction of four machine-learning models, SVM model exhibited the highest efficacy of prediction in the testing set (AUC = 0.952). SVM model containing five important variables can be used for prediction of AA.

CONCLUSION

This work provided a machine learning model containing five important variables, which may have good diagnostic efficiency for pediatric AA.

What Is Hot and New in Basic and Translational Science in Liver Transplantation in 2023? Report of the Basic and Translational Research Committee of the International Liver Transplantation Society

The 2023 Joint Annual Congress of the International Liver Transplantation Society, European Liver and Intestine Transplant Association, and Liver Intensive Care Group of Europe were held in Rotterdam, the Netherlands, from May 3 to 6, 2023. This year, all speakers were invited to attend the Congress in person for the first time since the COVID-19 pandemic. The congress was attended by 1159 registered delegates from 54 countries representing 5 continents, with the 10 countries comprising the bulk of the delegates. Of the 647 abstracts initially submitted, 542 were eventually presented at the meeting, coming from 38 countries (mainly North America, Europe, and Asia) and 85% of them (462 abstracts) came from only 10 countries. Fifty-three (9.8%) abstracts, originated from 17 countries, were submitted under the Basic/Translational Scientific Research category, a similar percentage as in 2022. Abstracts presented at the meeting were classified as (1) ischemia and reperfusion injury, (2) machine perfusion, (3) bioengineering and liver regeneration, (4) transplant oncology, (5) novel biomarkers in liver transplantation, (6) liver immunology (rejection and tolerance), and (7) artificial intelligence and machine learning. Finally, we evaluated the number of abstracts commented in the Basic and Translational Research Committee-International Liver Transplantation Society annual reports over the past 5 y that resulted in publications in peer-reviewed journals to measure their scientific impact in the field of liver transplantation.

Machine learning in liver surgery: Benefits and pitfalls

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians' and patients' understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards.

The role of echocardiographic assessment for the risk of adverse events in liver transplant recipients: A systematic review and meta-analysis

BACKGROUND & AIMS

Echocardiographic findings may provide valuable information about the cardiac conditions in cirrhotic patients waiting for liver transplantation (LT). However, data on the ability of the different echocardiographic parameters to predict post-transplant risk of mortality are scarce and heterogeneous. This systematic review evaluates the role of different echocardiographic features as predictors of post-LT mortality. A meta-analysis was also performed according to the observed results.

METHODS

A systematic review was conducted according to PRISMA guidelines. Medline (PubMed) database was searched through February 2023 for relevant published original articles reporting the prognostic value of echocardiographic findings associated with outcomes of adult LT recipients. The risk of bias in included articles was assessed using ROBINS-E tool. Methodological quality varied from low to high across the risk of bias domains.

RESULTS

Twenty-three studies were identified after the selection process; ten were enrollable for the meta-analyses. According to the results observed, the E/A ratio fashioned as a continuous value (HR = 0.43, 95%CI = 0.25-0.76; P = 0.003), and tricuspid regurgitation (HR = 2.36, 95%CI = 1.05-5.31; P = 0.04) were relevant predicting variables for post-LT death. Other echocardiographic findings failed to merge with statistical relevance.

CONCLUSION

Tricuspid regurgitation and left ventricular diastolic dysfunction play a role in the prediction of post-LT death. More studies are needed to clarify further the impact of these echocardiographic features in the transplantation setting.

Quality measures in pre-liver transplant care by the Practice Metrics Committee of the American Association for the Study of Liver Diseases

The liver transplantation (LT) evaluation and waitlisting process is subject to variations in care that can impede quality. The American Association for the Study of Liver Diseases (AASLD) Practice Metrics Committee (PMC) developed quality measures and patient-reported experience measures along the continuum of pre-LT care to reduce care variation and guide patient-centered care. Following a systematic literature review, candidate pre-LT measures were grouped into 4 phases of care: referral, evaluation and waitlisting, waitlist management, and organ acceptance. A modified Delphi panel with content expertise in hepatology, transplant surgery, psychiatry, transplant infectious disease, palliative care, and social work selected the final set. Candidate patient-reported experience measures spanned domains of cognitive health, emotional health, social well-being, and understanding the LT process. Of the 71 candidate measures, 41 were selected: 9 for referral; 20 for evaluation and waitlisting; 7 for waitlist management; and 5 for organ acceptance. A total of 14 were related to structure, 17 were process measures, and 10 were outcome measures that focused on elements not typically measured in routine care. Among the patient-reported experience measures, candidates of LT rated items from understanding the LT process domain as the most important. The proposed pre-LT measures provide a framework for quality improvement and care standardization among candidates of LT. Select measures apply to various stakeholders such as referring practitioners in the community and LT centers. Clinically meaningful measures that are distinct from those used for regulatory transplant reporting may facilitate local quality improvement initiatives to improve access and quality of care.

Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges

Hepatic artery thrombosis (HAT) is a devastating vascular complication following liver transplantation, requiring prompt diagnosis and rapid revascularization treatment to prevent graft loss. At present, imaging modalities such as ultrasound, computed tomography, and magnetic resonance play crucial roles in diagnosing HAT. Although imaging techniques have improved sensitivity and specificity for HAT diagnosis, they have limitations that hinder the timely diagnosis of this complication. In this sense, the emergence of artificial intelligence (AI) presents a transformative opportunity to address these diagnostic limitations. The develo pment of machine learning algorithms and deep neural networks has demon strated the potential to enhance the precision diagnosis of liver transplant com plications, enabling quicker and more accurate detection of HAT. This article examines the current landscape of imaging diagnostic techniques for HAT and explores the emerging role of AI in addressing future challenges in the diagnosis of HAT after liver transplant.

Association between TyG index trajectory and new-onset lean NAFLD: a longitudinal study

Objective

The purpose of this manuscript is to identify longitudinal trajectories of changes in triglyceride glucose (TyG) index and investigate the association of TyG index trajectories with risk of lean nonalcoholic fatty liver disease (NAFLD).

Methods

Using data from 1,109 participants in the Health Management Cohort longitudinal study, we used Latent Class Growth Modeling (LCGM) to develop TyG index trajectories. Using a Cox proportional hazard model, the relationship between TyG index trajectories and incident lean NAFLD was analyzed. Restricted cubic splines (RCS) were used to visually display the dose-response association between TyG index and lean NAFLD. We also deployed machine learning (ML) via Light Gradient Boosting Machine (LightGBM) to predict lean NAFLD, validated by receiver operating characteristic curves (ROCs). The LightGBM model was used to create an online tool for medical use. In addition, NAFLD was assessed by abdominal ultrasound after excluding other liver fat causes.

Results

The median age of the population was 46.6 years, and 440 (39.68%) of the participants were men. Three distinct TyG index trajectories were identified: "low stable" (TyG index ranged from 7.66 to 7.71, n=206, 18.5%), "moderate stable" (TyG index ranged from 8.11 to 8.15, n=542, 48.8%), and "high stable" (TyG index ranged from 8.61 to 8.67, n=363, 32.7%). Using a "low stable" trajectory as a reference, a "high stable" trajectory was associated with an increased risk of lean-NAFLD (HR: 2.668, 95% CI: 1.098-6.484). After adjusting for baseline age, WC, SBP, BMI, and ALT, HR increased slightly in "moderate stable" and "high stable" trajectories to 1.767 (95% CI:0.730-4.275) and 2.668 (95% CI:1.098-6.484), respectively. RCS analysis showed a significant nonlinear dose-response relationship between TyG index and lean NAFLD risk (χ2 = 11.5, P=0.003). The LightGBM model demonstrated high accuracy (Train AUC 0.870, Test AUC 0.766). An online tool based on our model was developed to assist clinicians in assessing lean NAFLD risk.

Conclusion

The TyG index serves as a promising noninvasive marker for lean NAFLD, with significant implications for clinical practice and public health policy.

The triglyceride-glucose index: a novel predictor of stroke and all-cause mortality in liver transplantation recipients

BACKGROUND

The triglyceride-glucose (TyG) index, identified as a reliable indicator of insulin resistance (IR), was reported to be associated with stroke recurrence and morbidity in the general population and critically ill patients. However, the relationship in liver transplantation (LT) recipients remains unknown. This study aimed to investigate the correlation between the TyG index and post-LT stroke along with all-cause mortality and further assess the influence of IR on the LT recipients' prognosis.

METHODS

The retrospective cohort study enrolled 959 patients who underwent LT at a university-based medical centre between January 2015 and January 2021. The participants were divided into three groups according to their TyG index tertiles. The primary outcome was post-LT stroke. Multivariate logistic regression, COX proportional hazards regression, and restricted cubic spline RCS were used to examine the association between the TyG index and outcomes in LT recipients.

RESULTS

With a median TyG index of 8.23 (7.78-8.72), 780 (87.18% males) patients were eventually included. The incidence of post-LT stroke was 5.38%, and the in-hospital, 1-year, and 3-year mortality rates were 5.54%, 13.21%, and 15.77%, respectively. Multivariate regression analysis showed an independent association between the TyG index and an increased risk of post-LT stroke [adjusted odds ratio (aOR), 3.398 (95% confidence interval [CI]: 1.371-8.426) P = 0. 008], in-hospital mortality [adjusted hazard ratio (aHR), 2.326 (95% CI: 1.089-4.931) P = 0.025], 1-year mortality [aHR, 1.668 (95% CI: 1.024-2.717) P = 0.039], and 3-year mortality [aHR, 1.837 (95% CI: 1.445-2.950) P = 0.012]. Additional RCS analysis also suggested a linear increase in the risk of postoperative stroke with elevated TyG index (P for nonlinearity = 0.480).

CONCLUSIONS

The TyG index may be a valuable and reliable indicator for assessing stroke risk and all-cause mortality in patients undergoing LT, suggesting its potential relevance in improving risk stratification during the peri-LT period.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC.

METHODS

The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression.

RESULTS

HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis.

CONCLUSIONS

Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Prognostic models in end stage liver disease

Cirrhosis is a major cause of death worldwide, and is associated with significant health care costs. Even if milestones have been recently reached in understanding and managing end-stage liver disease (ESLD), the disease course remains somewhat difficult to prognosticate. These difficulties have already been acknowledged already in the past, when scores instead of single parameters have been proposed as valuable tools for short-term prognosis. These standard scores, like Child Turcotte Pugh (CTP) and model for end-stage liver disease (MELD) score, relying on biochemical and clinical parameters, are still widely used in clinical practice to predict short- and medium-term prognosis. The MELD score, which remains an accurate, easy-to-use, objective predictive score, has received significant modifications over time, in order to improve its performance especially in the liver transplant (LT) setting, where it is widely used as prioritization tool. Although many attempts to improve prognostic accuracy have failed because of lack of replicability or poor benefit with the comparator (often the MELD score or its variants), few scores have been recently proposed and validated especially for subgroups of patients with ESLD, as those with acute-on-chronic liver failure. Artificial intelligence will probably help hepatologists in the near future to fill the current gaps in predicting disease course and long-term prognosis of such patients.

Hepatocellular Carcinoma: Optimal Radiological Evaluation before Liver Transplantation

Liver transplantation (LT) is the recommended curative-intent treatment for patients with early or intermediate-stage hepatocellular carcinoma (HCC) who are ineligible for resection. Imaging plays a central role in staging and for selecting the best LT candidates. This review will discuss recent developments in pre-LT imaging assessment, in particular LT eligibility criteria on imaging, the technical requirements and the diagnostic performance of imaging for the pre-LT diagnosis of HCC including the recent Liver Imaging Reporting and Data System (LI-RADS) criteria, the evaluation of the response to locoregional therapy, as well as the non-invasive prediction of HCC aggressiveness and its impact on the outcome of LT. We will also briefly discuss the role of nuclear medicine in the pre-LT evaluation and the emerging role of artificial intelligence models in patients with HCC.

Machine Learning-Assisted High-Throughput Strategy for Real-Time Detection of Spermine Using a Triple-Emission Ratiometric Probe

In this study, we designed and fabricated a spermine-responsive triple-emission ratiometric fluorescent probe using dual-emissive carbon nanoparticles and quantum dots, which improve the sensor's accuracy and reduce interfering environmental effects. The probe is advantageous for the proportionate detection of spermine because it has good emission resolution, and the maximum points of the two emission peaks differ by 95 nm. As a proof of concept, cuvettes and a 96-well plate were combined with a smartphone and YOLO series algorithms to accomplish real-time, visual, and high-throughput detection of seafood and meat freshness. In addition, the reaction mechanism was verified by density functional theory and fundamental characterizations. Upon exposure to different amounts of spermine, the intensity of the fluorescent probe changed linearly, and the fluorescent color shifted from yellow-green to red, with a limit of detection of 0.33 μM. To enable visual identification of food-originated spermine, a hydrogel-based visual sensing platform was successfully developed utilizing the triple-emission fluorescent probe. Consequently, spermine could be identified and quantified without complicated equipment.

Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons

Background

The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.

Material and method

Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated "in situ" for transplantation, and those discarded after the "in situ" evaluation were considered as no transplantable liver grafts, while those grafts transplanted after "in situ" evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed.

Results

A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85.

Conclusion

The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

Artificial Intelligence Advances in Transplant Pathology

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.