The Role of Machine Learning in Cardiovascular Pathology

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole slide images. Machine learning tools have incredible potential to standardize, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe here the principles of these tools and technologies and some successful pre-clinical and pre-translational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterizing cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. In fully realizing the value of these tools in clinical cardiovascular pathology, we have identified three essential challenges. First is image quality standardization to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don't always agree, and thus truth may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large enough data sets to facilitate robust algorithm development, necessitating large, cross-institutional, shared image databases. The power of histopathology-based machine learning technologies is tremendous; we outline here the next steps needed to capitalize on this power.