Comparison of AI techniques for prediction of liver fibrosis in hepatitis patients

Mobile Diagnostic Clinics

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Artificial intelligence in the diagnosis of cirrhosis and portal hypertension

Clinically significant portal hypertension is associated with an increased risk of developing gastroesophageal varices and hepatic decompensation. Hepatic venous pressure gradient measurement and esophagogastroduodenoscopy are the gold-standard methods for assessing clinically significant portal hypertension and gastroesophageal varices, respectively. However, invasiveness, cost, and feasibility limit their widespread use, especially if repeated and serial evaluations are required to assess the efficacy of pharmacotherapy. Artificial intelligence describes a range of techniques that allow machines to perform tasks typically thought to require human reasoning and problem-solving skills. Artificial intelligence has made great strides in the field of medicine, and is also involved in portal hypertension diagnosis. Artificial intelligence tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time. This review focuses on the recent advances in artificial intelligence for the noninvasive diagnosis of portal hypertension and gastroesophageal varices and monitoring of risk assessment of its complications in clinical practice.

An overview of clinical decision support systems: benefits, risks, and strategies for success

Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice-Aided Diagnosis: Interrupted Time Series Study

BACKGROUND

Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial.

OBJECTIVE

The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice-aided diagnosis in real-world research.

METHODS

This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019.

RESULTS

The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all P<.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, P=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, P=.01). Similar results were obtained in the subgroup analysis.

CONCLUSIONS

The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians' diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.

Validation of Novel Fibrosis Index (NFI) for assessment of liver fibrosis: comparison with transient elastography (FibroScan)

Background

In this study, we collated cheap and readily available non-invasive biomarkers and FibroScan score in predicting fibrosis stages in chronic hepatitis C virus (HCV) infection.

Methods

We studied 1898 patients with HCV infection confirmed by presence of HCV RNA in their serum. We compared the FibroScan score and fibrosis indices (FIs): aspartate transaminase (AST) to alanine transaminase (ALT) ratio (AAR), AST to Platelet Ratio Index (APRI), FI, fibrosis-4 (FIB-4), Age-Platelet Index (API), Pohl score, Fibrosis Cirrhosis Index (FCI). We developed a new FI, named Novel Fibrosis Index (NFI) calculated by the following formula: NFI=[(bilirubin×(ALP)²)/(platelet count (albumin)²)]−n.

Results

AAR, APRI, FI, FIB-4, API, Pohl score, FCI and NFI were able to predict fibrosis stage with correlation coefficient indices 0.848, 0.711, 0.618, 0.741, 0.529, 0.360, 0.477 and 0.26, respectively. Receiver operating characteristic curves showed sensitivity and specificity for predicting F3 by NFI=75.1% and 41.1% and F4 for NFI=72.1% and 47.1%, AAR=62.8% and 37.6%, APRI=74.6% and 87.6%, FIB-4=53.2% and 72.3%, FI=78.1% and 92.3%, API=78.1% and 60%, Pohl score=38.1% and 78.1% and FCI=78.1% and 88.1%.

Conclusions

Our NFI predicted F3 and has been found to have more sensitivity and specificity in predicting F4 fibrosis stage than other FIs.

Universal Index for Cirrhosis (UIC index): The development and validation of a novel index to predict advanced liver disease

Aim

The purpose of this study was to create and validate a novel serological diagnostic index to predict cirrhosis of all etiologies.

Methods

This was a retrospective observational study of 771 patients, age >18 years, who underwent a liver biopsy. The stage of fibrosis and routine laboratory values were recorded. The data were randomly separated into 2 datasets (training 50% and testing 50%). A stepwise logistic regression model was used to develop the novel index. The area under the curve of receiver operating characteristic (AUROC) was applied to compare the new index to existing ones (Fibro-Q, FIB4, APRI, AAR), which was also validated in the testing dataset.

Results

Variables associated with the presence of cirrhosis were first assessed by univariate analysis then by multivariable analysis, which indicated serum glutamic-oxaloacetic acid transaminase, serum glutamic-pyruvic transaminase, international normalized ratio, albumin, blood urea nitrogen, glucose, platelet count, total protein, age, and race were the independent predictors of cirrhosis (P<0.05). Regression formula for prediction of cirrhosis was generated and a novel index was subsequently created. The diagnostic performance of the novel index for predicting cirrhosis was assessed using the receiver operating characteristic curve. The new index had significantly higher AUROC (0.83, 95% CI: 0.79–0.87) than Fibro-Q (0.80, 95% CI: 0.76–0.85), FIB4 (0.79, 95% CI: 0.74–0.83), APRI (0.74, 95% CI: 0.69–0.78), and AAR (0.72, 95% CI: 0.67–0.78).

Conclusion

The novel index had the highest AUROC curve when compared with current indices and can be applied to all etiologies of chronic liver disease.