Machine learning to distinguish lymphangioleiomyomatosis from other diffuse cystic lung diseases.

Patients with lymphangioleiomyomatosis (LAM) frequently experience delays in diagnosis, owing partly to the delayed characterization of imaging findings. This project aimed to develop a machine learning model to distinguish LAM from other diffuse cystic lung diseases (DCLDs). Computed tomography scans from patients with confirmed DCLDs were acquired from registry datasets and a recurrent convolutional neural network was trained for their classification. The final model provided sensitivity and specificity of 85% and 92%, respectively, for LAM, similar to the historical metrics of 88% and 97%, respectively, by experts. The proof-of-concept work holds promise as a clinically useful tool to assist in recognizing LAM.