CT-based Machine Learning for Donor Lung Screening Prior to Transplantation

Abstract

Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation. Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant. Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.

The transformative potential of artificial intelligence in solid organ transplantation

“…The algorithm was tested on 1,226 external datasets and performed on par with manual segmentation, with high Dice similarity metrics for both cortex and medulla segmentation. Along the same lines, Ram et al explored the use of CT scans and supervised "dictionary learning" approach for screening donor lungs before transplantation (5). The algorithm was trained to detect pulmonary abnormalities on CT scans and predict post-transplant outcomes.…”

Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression

“…For image classification problems a separate class-specific dictionary is learnt from patches belonging to each class of images. In this work, we have developed a multiview task-driven dictionary learning algorithm – a novel approach that aims to learn discriminative dictionaries for each class from multiple views of the data in a joint fashion by imposing group sparsity constraints 34 .…”