Automated deidentification of radiology reports combining transformer and "hide in plain sight" rule-based methods.

Objective: To develop an automated deidentification pipeline for radiology reports that detect protected health information (PHI) entities and replaces them with realistic surrogates "hiding in plain sight."

Materials And Methods: In this retrospective study, 999 chest X-ray and CT reports collected between November 2019 and November 2020 were annotated for PHI at the token level and combined with 3001 X-rays and 2193 medical notes previously labeled, forming a large multi-institutional and cross-domain dataset of 6193 documents. Two radiology test sets, from a known and a new institution, as well as i2b2 2006 and 2014 test sets, served as an evaluation set to estimate model performance and to compare it with previously released deidentification tools.

Predicting Patient Demographics From Chest Radiographs With Deep Learning.

Background: Deep learning models are increasingly informing medical decision making, for instance, in the detection of acute intracranial hemorrhage and pulmonary embolism. However, many models are trained on medical image databases that poorly represent the diversity of the patients they serve. In turn, many artificial intelligence models may not perform as well on assisting providers with important medical decisions for underrepresented populations.

Evaluation of Automated Public De-Identification Tools on a Corpus of Radiology Reports.

Purpose: To evaluate publicly available de-identification tools on a large corpus of narrative-text radiology reports.

Materials And Methods: In this retrospective study, 21 categories of protected health information (PHI) in 2503 radiology reports were annotated from a large multihospital academic health system, collected between January 1, 2012 and January 8, 2019. A subset consisting of 1023 reports served as a test set; the remainder were used as domain-specific training data.

Detection of Muscle Tension Dysphonia Using Eulerian Video Magnification: A Pilot Study.

Objective: To determine whether Eulerian Video Magnification software is useful in diagnosis of muscle tension dysphonia (MTD).

Study Design: Prospective.

Methods: Adult patients scheduled in a tertiary care laryngology practice for evaluation of dysphonia were recruited between November 2016 and March 2017.

A Prospective, Longitudinal Study of Outcomes Following Total Knee Arthroplasty Stratified by Gender.

This study aimed to compare temporal trends in outcomes between men and women following TKA. A database of 272 TKAs at 7 years follow-up were evaluated for survivorship, functional, activity, and quality of life outcomes. Evaluation of implant survivorship, KSS scores, and ROM showed no significance differences between men and women, but both functional KSS scores and LEAS showed that men had significantly higher functional scores.