Assessment of facial video-based detection of atrial fibrillation across human complexion.

Background: Early self-detection of atrial fibrillation (AF) can help delay and/or prevent significant associated complications, including embolic stroke and heart failure. We developed a facial video technology, videoplethysmography (VPG), to detect AF based on the analysis of facial pulsatile signals. Objective: The purpose of this study was to evaluate the accuracy of a video-based technology to detect AF on a smartphone and to test the performance of the technology in AF patients across the whole spectrum of skin complexion and under various recording conditions. Methods: The performance of video-based monitoring depends on a set of factors such as the angle and the distance between the camera and the patient's face, the strength of illumination, and the patient's skin tone. We conducted a clinical study involving 60 subjects with a confirmed diagnosis of AF. A continuous electrocardiogram was used as the gold standard for cardiac rhythm annotation. The VPG technology was fine-tuned on a smartphone for the first 15 subjects. Validation recordings were then done using 7053 measurements collected from the remaining 45 subjects. Results: The VPG technology detected the presence of AF using the video camera from a common smartphone with sensitivity and specificity ≥90%. The ambient level of illumination needs to be ≥100 lux for the technology to deliver consistent performance across all skin tones. Conclusion: We demonstrated that facial video-based detection of AF provides accurate outpatient cardiac monitoring including high pulse rate accuracy and medical-grade performance for AF detection.

What Do Reported Learning Curves Mean for Orthopaedic Surgeons?

Practicing orthopaedic surgeons must assess the effects of the learning curve on patient safety and surgical outcomes if a new implant, technique, or approach is being considered; however, it remains unclear how learning curves reported in the literature should be interpreted and to what extent their results can be generalized. Learning curve reports from other surgical specialties and from orthopaedic surgery can be analyzed to identify the strengths and weaknesses of learning curve reporting. Single-surgeon series and registry data can be analyzed to understand learning challenges and to develop a personalized learning plan. Learning curve reports from single-surgeon series have several limitations that result from the limited dataset reported and inconsistencies in the way data are reported. Conversely, learning curve reports from registry data are likely to have greater generalizability, but are largely beneficial retrospectively, after data from a sufficient number of surgeons are assessed. There is a pressing need for surgeons to develop improved and consistent standards for learning curve reporting. Although registry data may provide better prospective measures in the future, the implementation of such registries faces several challenges. Despite substantial limitations, single-surgeon series remain the most effective way for practicing surgeons to assess their learning challenge and develop an appropriate learning plan.