RESUMO
Introduction/Aims: Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects. Methods: Quadriceps complex (QC [rectus femoris and vastus intermedius]) and tibialis anterior (TA) muscle ultrasound images of healthy, intensive care unit, and/or lung cancer subjects were captured with portable devices. Automated analyses of muscle morphometry were performed using a custom-built deep-learning model (MyoVision-US), while manual analyses were performed by experts. Consistency between manual and automated analyses was determined using intraclass correlation coefficients (ICC), while predictability of MyoVision -US was calculated using adjusted linear regression (adj.R 2 ). Results: Manual analysis took approximately 24 hours to analyze all 180 images, while MyoVision - US took 247 seconds, saving roughly 99.8%. Consistency between the manual and automated analyses by ICC was good to excellent for all QC (ICC:0.85-0.99) and TA (ICC:0.93-0.99) measurements, even for critically ill (ICC:0.91-0.98) and lung cancer (ICC:0.85-0.99) images. The predictability of MyoVision-US was moderate to strong for QC (adj.R 2 :0.56-0.94) and TA parameters (adj.R 2 :0.81-0.97). Discussion: The application of AI automating lower limb muscle ultrasound analyses showed excellent consistency and strong predictability compared with human analysis. Future work needs to explore AI-powered models for the evaluation of other skeletal muscle groups.
RESUMO
Digital pathology applications present several challenges, including the processing, storage, and distribution of gigapixel images across distributed computational resources and viewing stations. Individual slides must be available for interactive review, and large repositories must be programmatically accessible for dataset and model building. We present a platform to manage and process multi-modal pathology data (images and case information) across multiple locations. Using an agent-based system coupled with open-source automated machine learning and review tools allows not only dynamic load-balancing and cross-network operation but also the development of research and clinical AI models using the data managed by the platform. The platform presented covers end-to-end AI workflow from data acquisition and curation through model training and evaluation allowing for sharing and review. We conclude with a case study of colon and prostate cancer model development utilizing the presented system.