Deep learning-based detection of intravenous contrast in computed tomography scans

Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) and lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans manually annotated for contrast presence by clinical experts. Five different DL models were adopted and trained in HN training datasets for slice-level contrast detection. Model performances were evaluated on a hold-out set and on an independent validation set from another institution. DL models was then fine-tuned on chest CT data and externally validated on a separate chest CT dataset. Results: Initial DICOM metadata tags for IV contrast were missing or erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the best overall detection performance. For HN scans, AUC was 0.996 in the internal validation set (n = 216) and 1.0 in the external validation set (n = 595). The fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation set (n = 53), and AUC: 0.980 for the external validation set (n = 402). Conclusion: The DL model could accurately detect IV contrast in both HN and chest CT scans with near-perfect performance.

An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications

Objective: Individuals with spinal cord injury (SCI) report upper limb function as their top recovery priority. To accurately represent the true impact of new interventions on patient function and independence, evaluation should occur in a natural setting. Wearable cameras can be used to monitor hand function at home, using computer vision to automatically analyze the resulting videos (egocentric video). A key step in this process, hand detection, is difficult to do robustly and reliably, hindering deployment of a complete monitoring system in the home and community. We propose an accurate and efficient hand detection method that uses a simple combination of existing detection and tracking algorithms. Methods: Detection, tracking, and combination methods were evaluated on a new hand detection dataset, consisting of 167,622 frames of egocentric videos collected on 17 individuals with SCI performing activities of daily living in a home simulation laboratory. Results: The F1-scores for the best detector and tracker alone (SSD and Median Flow) were 0.90$\pm$0.07 and 0.42$\pm$0.18, respectively. The best combination method, in which a detector was used to initialize and reset a tracker, resulted in an F1-score of 0.87$\pm$0.07 while being two times faster than the fastest detector alone. Conclusion: The combination of the fastest detector and best tracker improved the accuracy over online trackers while improving the speed of detectors. Significance: The method proposed here, in combination with wearable cameras, will help clinicians directly measure hand function in a patient's daily life at home, enabling independence after SCI.