Eye-Tracking Metrics as an Indicator of Workload in Commercial Single-Pilot Operations

There is a current trend in commercial aviation that points toward a possible transition from two-crew to single-pilot operations (SPO). The workload on the single pilot is expected to be a major issue for SPO. In order to find the best support solutions for the pilot in SPO, a thorough understanding of pilot workload is required. The present study aims at evaluating pilot workload by means of eye-tracking metrics. A flight simulator study was conducted with commercial pilots. Their task was to fly short approach and landing scenarios with or without the support of a second pilot. The results showed that fixation frequencies were higher during SPO, average dwell durations decreased, and participants transitioned more frequently between different areas of interest. These results suggest that particularly the temporal demand might be an issue for a possible transition to SPO. The eye-tracking metrics support results obtained from subjective workload ratings

Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations

Predictions of patient outcomes after a given therapy are fundamental to medical practice. We employ a machine learning approach towards predicting the outcomes after stereotactic radiosurgery for cerebral arteriovenous malformations (AVMs). Using three prospective databases, a machine learning approach of feature engineering and model optimization was implemented to create the most accurate predictor of AVM outcomes. Existing prognostic systems were scored for purposes of comparison. The final predictor was secondarily validated on an independent site’s dataset not utilized for initial construction. Out of 1,810 patients, 1,674 to 1,291 patients depending upon time threshold, with 23 features were included for analysis and divided into training and validation sets. The best predictor had an average area under the curve (AUC) of 0.71 compared to existing clinical systems of 0.63 across all time points. On the heldout dataset, the predictor had an accuracy of around 0.74 at across all time thresholds with a specificity and sensitivity of 62% and 85% respectively. This machine learning approach was able to provide the best possible predictions of AVM radiosurgery outcomes of any method to date, identify a novel radiobiological feature (3D surface dose), and demonstrate a paradigm for further development of prognostic tools in medical care