Browsing by Author "Prager, Ross"
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- ItemAutomated real-time detection of lung sliding using artificial intelligence: a prospective diagnostic accuracy study(2024) Clausdorff Fiedler, Hans Jurgen; Prager, Ross; Smith, Delaney; Wu, Derek; Dave, Chintan; Tschirhart, Jared; Wu, Ben; VanBerlo, Blake; Malthaner, Richard; Arntfield, RobertAs mental health issues continue to rise in Latin America, the need for research in this field becomes increasingly pressing. This study aimed to explore the perceived barriers and resources for research and publications among psychiatrists and psychiatry trainees from nine Spanish-speaking countries in South America. Data was collected through an anonymous online survey and analyzed using descriptive methods and the SPSS Statistical package. In total, 214 responses were analyzed. Among the participating psychiatrists, 61.8% reported having led a research project and 74.7% of them reported having led an academic publication. As for the psychiatry trainees, 26% reported having conducted research and 41.5% reported having published or attempted to publish an academic paper. When available, having access to research training, protected research time and mentorship opportunities were significant resources for research. Further support is needed in terms of funding, training, protected research time and mentorship opportunities. However, despite their efforts to participate in the global mental health discussion, Latin American psychiatrists and psychiatry trainees remain largely underrepresented in the literature.
- ItemImproving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification(2024) Wu, Derek; Smith, Delaney; VanBerlo, Blake; Roshankar, Amir; Lee, Hoseok; Li, Brian; Ali, Faraz; Rahman, Marwan; Basmaji, John; Tschirhart, Jared; Ford, Alex; VanBerlo, Bennett; Durvasula, Ashritha; Vannelli, Claire; Dave, Chintan; Deglint, Jason; Ho, Jordan; Chaudhary, Rushil; Clausdorff Fiedler, Hans Jurgen; Prager, Ross; Millington, Scott; Shah, Samveg; Buchanan, Brian; Arntfield, RobertDeep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce—compared to other medical imaging data—we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model’s performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.