2024
Khodagholi, Zahra; Sun, Jasmine; Awad, Nour; Vankayalapati, Amulya; Dion, Gregory R.; Brattain, Laura J.
Ultrasound Landmark Tracking for Real-Time Anatomcial Airway Identification and Reliability Check Conference
2024 IEEE 20th International Conference on Body Sensor Networks (BSN), 2024.
Abstract | Tags: BSN, REU | Links:
@conference{Khodagholi2024,
title = {Ultrasound Landmark Tracking for Real-Time Anatomcial Airway Identification and Reliability Check},
author = {Zahra Khodagholi and Jasmine Sun and Nour Awad and Amulya Vankayalapati and Gregory R. Dion and Laura J. Brattain},
url = {https://ieeexplore.ieee.org/document/10780557},
doi = {https://doi.org/10.1109/BSN63547.2024.10780557},
year = {2024},
date = {2024-10-15},
urldate = {2024-10-15},
publisher = {2024 IEEE 20th International Conference on Body Sensor Networks (BSN)},
abstract = {Ultrasound has become an essential tool for airway management, providing non-invasive, real-time imaging that enhances the procedure efficacy. The main challenges of ultrasound include the specialized training needed for interpretation and the high patient variability. This paper presents ULTRA-AIR (Ultrasound Landmark Tracking for Real-time Anatomical Airway Identification and Reliability Check), a deep learning system designed for real-time tracking of multiple neck anatomical landmarks to assist with ultrasound-guided airway management. Uncertainty estimation was also computed to quantify model confidence scores, providing safety measures for trustworthy AI in real-time. Utilizing the YOLOv9 architecture, ULTRA-AIR was fine-tuned using Leave-One-Out cross-validation on neck ultrasound data collected from six human subjects, total of 3,060 images and 11,142 annotated bounding boxes. The system achieved AUCs of 0.99, 0.95, 0.89, and 0.77 for the classes of strap muscle, thyroid gland, tracheal ring, and thyroid cartilage, respectively. Average F1 score was 0.83 and mAP50 was 0.84. High uncertainty scores helped to identify cases needing human intervention. ULTRA-AIR shows promise for safe, accurate, and rapid airway assessments at point of care.},
keywords = {BSN, REU},
pubstate = {published},
tppubtype = {conference}
}
Ultrasound has become an essential tool for airway management, providing non-invasive, real-time imaging that enhances the procedure efficacy. The main challenges of ultrasound include the specialized training needed for interpretation and the high patient variability. This paper presents ULTRA-AIR (Ultrasound Landmark Tracking for Real-time Anatomical Airway Identification and Reliability Check), a deep learning system designed for real-time tracking of multiple neck anatomical landmarks to assist with ultrasound-guided airway management. Uncertainty estimation was also computed to quantify model confidence scores, providing safety measures for trustworthy AI in real-time. Utilizing the YOLOv9 architecture, ULTRA-AIR was fine-tuned using Leave-One-Out cross-validation on neck ultrasound data collected from six human subjects, total of 3,060 images and 11,142 annotated bounding boxes. The system achieved AUCs of 0.99, 0.95, 0.89, and 0.77 for the classes of strap muscle, thyroid gland, tracheal ring, and thyroid cartilage, respectively. Average F1 score was 0.83 and mAP50 was 0.84. High uncertainty scores helped to identify cases needing human intervention. ULTRA-AIR shows promise for safe, accurate, and rapid airway assessments at point of care.
Khodagholi, Zahra; Sun, Jasmine; Awad, Nour; Vankayalapati, Amulya; Dion, Gregory R.; Brattain, Laura J.
Ultrasound Landmark Tracking for Real-Time Anatomcial Airway Identification and Reliability Check Conference
2024 IEEE 20th International Conference on Body Sensor Networks (BSN), 2024.
Abstract | Tags: BSN, REU | Links:
@conference{Khodagholi2024b,
title = {Ultrasound Landmark Tracking for Real-Time Anatomcial Airway Identification and Reliability Check},
author = {Zahra Khodagholi and Jasmine Sun and Nour Awad and Amulya Vankayalapati and Gregory R. Dion and Laura J. Brattain},
url = {https://ieeexplore.ieee.org/document/10780557},
doi = {https://doi.org/10.1109/BSN63547.2024.10780557},
year = {2024},
date = {2024-10-15},
urldate = {2024-10-15},
publisher = {2024 IEEE 20th International Conference on Body Sensor Networks (BSN)},
abstract = {Ultrasound has become an essential tool for airway management, providing non-invasive, real-time imaging that enhances the procedure efficacy. The main challenges of ultrasound include the specialized training needed for interpretation and the high patient variability. This paper presents ULTRA-AIR (Ultrasound Landmark Tracking for Real-time Anatomical Airway Identification and Reliability Check), a deep learning system designed for real-time tracking of multiple neck anatomical landmarks to assist with ultrasound-guided airway management. Uncertainty estimation was also computed to quantify model confidence scores, providing safety measures for trustworthy AI in real-time. Utilizing the YOLOv9 architecture, ULTRA-AIR was fine-tuned using Leave-One-Out cross-validation on neck ultrasound data collected from six human subjects, total of 3,060 images and 11,142 annotated bounding boxes. The system achieved AUCs of 0.99, 0.95, 0.89, and 0.77 for the classes of strap muscle, thyroid gland, tracheal ring, and thyroid cartilage, respectively. Average F1 score was 0.83 and mAP50 was 0.84. High uncertainty scores helped to identify cases needing human intervention. ULTRA-AIR shows promise for safe, accurate, and rapid airway assessments at point of care.},
keywords = {BSN, REU},
pubstate = {published},
tppubtype = {conference}
}
Ultrasound has become an essential tool for airway management, providing non-invasive, real-time imaging that enhances the procedure efficacy. The main challenges of ultrasound include the specialized training needed for interpretation and the high patient variability. This paper presents ULTRA-AIR (Ultrasound Landmark Tracking for Real-time Anatomical Airway Identification and Reliability Check), a deep learning system designed for real-time tracking of multiple neck anatomical landmarks to assist with ultrasound-guided airway management. Uncertainty estimation was also computed to quantify model confidence scores, providing safety measures for trustworthy AI in real-time. Utilizing the YOLOv9 architecture, ULTRA-AIR was fine-tuned using Leave-One-Out cross-validation on neck ultrasound data collected from six human subjects, total of 3,060 images and 11,142 annotated bounding boxes. The system achieved AUCs of 0.99, 0.95, 0.89, and 0.77 for the classes of strap muscle, thyroid gland, tracheal ring, and thyroid cartilage, respectively. Average F1 score was 0.83 and mAP50 was 0.84. High uncertainty scores helped to identify cases needing human intervention. ULTRA-AIR shows promise for safe, accurate, and rapid airway assessments at point of care.