Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds.

INTRODUCTION: The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML). METHODS: Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy. RESULTS: The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted. CONCLUSIONS: These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.

Accuracy of the triglyceride to high-density lipoprotein cholesterol ratio for prediction of the low-density lipoprotein phenotype B.

This study examined the accuracy of a triglyceride/high-density lipoprotein (HDL) cholesterol ratio of 3.8 for the prediction of low-density lipoprotein (LDL) phenotype B. The ratio of 3.8 was based on Adult Treatment Panel recommendations for normal fasting triglycerides (<150 mg/dl) and HDL cholesterol (>40 mg/dl). Fasting blood samples were obtained from 658 patients. LDL phenotype analysis was performed by nuclear magnetic resonance spectroscopy. A triglyceride/HDL cholesterol ratio of 3.8 divided the distribution of LDL phenotypes with 79% (95% confidence interval [CI] 74 to 83) of phenotype B greater than and 81% (95% CI 77 to 85) of phenotype A less than the ratio of 3.8. The ratio was reliable for identifying LDL phenotype B in men and women.