Browsing by Author "Adila, Dyah"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Artificial Intelligence to Accelerate COVID-19 Identification from Chest X-rays
    (2021-05) Adila, Dyah
    Importance: Clinical signs and symptoms for COVID-19 remain the mainstay of early diagnosis and initial management in the emergency department (ED) and inpatient setting at many hospitals due to de- lays in obtaining results of PCR testing and limitations in access to rapid antigen testing. The majority of many patients with COVID- 19 will present with respiratory symptoms necessitating a chest x-ray (CXR) as a routine part of screening. An AI-based model to predict COVID-19 likelihood from CXR findings can serve as an important and immediate adjunct to accelerate clinical decision making.Objective: To develop a robust AI-based diagnostic model to identify CXRs with COVID-19 compared with all non-COVID-19 CXRs. Setting: Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Va- Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-i 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International). Main Outcome and Measure: Model performance assessed via Area under the Receiver Operating Curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC). Results: Patients with COVID-19 had significantly higher COVID- 19 Diagnostic Scores than patients without COVID-19 on both real-time electronic health records and external (non-publicly available) validation. The model performed well across all four methods for model validation with AUROCs ranging between 0.7 – 0.96 and high PPV and specificity. The model performed had improved discrimination for patients with “severe” as compared to “moderate” COVID-19 disease. The model had unrealistic performance using publicly available databases, reflecting the inherent limitations in many previously developed models relying on publicly available data for training and validation. Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, to support COVID-19 diagnosis which largely hinges on exposure history, signs, and symptoms. Future research should focus on optimizing discrimination of “mild” COVID-19 from non-COVID-19 image findings.
  • Thumbnail Image
    Item
    Physics-Guided Anomalous Trajectory Detection: Technical Report
    (2020) Shrinivasa Nairy, Divya; Adila, Dyah; Li, Yan; Shekhar, Shashi
    Given ship trajectory data for a region, this paper proposes a physics-guided approach to detect anomalous trajectories. This problem is important for detection of illegal fishing or cargo transfer, which cause environmental and societal damage. This problem is challenging due to the presence of gaps in trajectories. Current state-of-the-art approaches either ignore the gaps or fill them using simple linear interpolation, which underestimates the ship’s possible locations during the gap. This paper proposes a novel physics-guided gap-aware anomaly detection test that incorporates physical constraints using a space-time prism. The proposed approach is evaluated with a case study using Marine Cadastre data of ships traversing in the Aleutian Islands region of Alaska in October 2017. A trajectory that could have traversed a marine protected area is correctly flagged by the proposed approach for investigation.