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.

International evaluation of an AI system for breast cancer screening.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.

Implementation of Certified EHR, Patient Portal, and "Direct" Messaging Technology in a Radiology Environment Enhances Communication of Radiology Results to Both Referring Physicians and Patients.

Since 2009, the Federal government distributed over $29 billion to providers who were adopting compliant electronic health record (EHR) technology. With a focus on radiology, we explore how EHR technology impacts interoperability with referring clinicians' EHRs and patient engagement. We also discuss the high-level details of contributing supporting frameworks, specifically Direct messaging and health information service provider (HISP) technology. We characterized Direct messaging, a secure e-mail-like protocol built to allow exchange of encrypted health information online, and the new supporting HISP infrastructure. Statistics related to both the testing and active use of this framework were obtained from DirectTrust.org, an organization whose framework supports Direct messaging use by healthcare organizations. To evaluate patient engagement, we obtained usage data from a radiology-centric patient portal between 2014 and 2015, which in some cases included access to radiology reports. Statistics from 2013 to 2015 showed a rise in issued secure Direct addresses from 8724 to 752,496; a rise in the number of participating healthcare organizations from 667 to 39,751; and a rise in the secure messages sent from 122,842 to 27,316,438. Regarding patient engagement, an average of 234,679 patients per month were provided portal access, with 86,400 patients per month given access to radiology reports. Availability of radiology reports online was strongly associated with increased system usage, with a likelihood ratio of 2.63. The use of certified EHR technology and Direct messaging in the practice of radiology allows for the communication of patient information and radiology results with referring clinicians and increases patient use of patient portal technology, supporting bidirectional radiologist-patient communication.