Clinical applicability of artificial intelligence for patients with an inherited heart disease: a scoping review

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time and resource intensive. To date, AI models have demonstrated immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have displayed ability to improve testing protocols, as through model identification of disease and genotype, specific clinical testing (e.g. drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of the field, particularly regarding the development and implementation of clinically applicable screening tools. This review summarizes key developments in the field, including studies in Long QT Syndrome, Brugada Syndrome, Hypertrophic Cardiomyopathy, and Arrhythmogenic Cardiomyopathies, and provides direction for effective future AI implementation in clinical practice.

Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records

BACKGROUND

Conducting a systematic review demands a significant amount of effort in screening titles and abstracts. To accelerate this process, various tools that utilize active learning have been proposed. These tools allow the reviewer to interact with machine learning software to identify relevant publications as early as possible. The goal of this study is to gain a comprehensive understanding of active learning models for reducing the workload in systematic reviews through a simulation study.

METHODS

The simulation study mimics the process of a human reviewer screening records while interacting with an active learning model. Different active learning models were compared based on four classification techniques (naive Bayes, logistic regression, support vector machines, and random forest) and two feature extraction strategies (TF-IDF and doc2vec). The performance of the models was compared for six systematic review datasets from different research areas. The evaluation of the models was based on the Work Saved over Sampling (WSS) and recall. Additionally, this study introduces two new statistics, Time to Discovery (TD) and Average Time to Discovery (ATD).

RESULTS

The models reduce the number of publications needed to screen by 91.7 to 63.9% while still finding 95% of all relevant records (WSS@95). Recall of the models was defined as the proportion of relevant records found after screening 10% of of all records and ranges from 53.6 to 99.8%. The ATD values range from 1.4% till 11.7%, which indicate the average proportion of labeling decisions the researcher needs to make to detect a relevant record. The ATD values display a similar ranking across the simulations as the recall and WSS values.

CONCLUSIONS

Active learning models for screening prioritization demonstrate significant potential for reducing the workload in systematic reviews. The Naive Bayes + TF-IDF model yielded the best results overall. The Average Time to Discovery (ATD) measures performance of active learning models throughout the entire screening process without the need for an arbitrary cut-off point. This makes the ATD a promising metric for comparing the performance of different models across different datasets.

Homozygous Pro1066Arg MYBPC3 Pathogenic Variant in a 26Mb Region of Homozygosity Associated with Severe Hypertrophic Cardiomyopathy in a Patient of an Apparent Non-Consanguineous Family

MYPBC3 and MYH7 are the most frequently mutated genes in patients with hereditary HCM. Homozygous and compound heterozygous genotypes generate the most severe phenotypes. A 35-year-old woman who was a homozygous carrier of the p.(Pro1066Arg) variant in the MYBPC3 gene, developed HCM phenocopy associated with left ventricular noncompaction and various degrees of conduction disease. Her father, a double heterozygote for this variant in MYBPC3 combined with the variant p.(Gly1931Cys) in the MYH7 gene, was affected by HCM. The variant in MYBPC3 in the heterozygosis-produced phenotype was neither in the mother nor in her only sister. Familial segregation analysis showed that the homozygous genotype p.(Pro1066Arg) was located in a region of 26 Mb loss of heterozygosity due to some consanguinity in the parents. These findings describe the pathogenicity of this variant, supporting the hypothesis of cumulative variants in cardiomyopathies, as well as the modulatory effect of the phenotype by other genes such as MYH7. Advancing HPO phenotyping promoted by the Human Phenotype Ontology, the gene-disease correlation, and vice versa, is evidence for the phenotypic heterogeneity of familial heart disease. The progressive establishment of phenotypic characteristics over time also complicates the clinical description.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.