Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound

Sultan, Laith R.; Haertter, Allison; Al-Hasani, Maryam; Demiris, George; Cary, Theodore W.; Tung-Chen, Yale; Sehgal, Chandra M.

doi:10.3390/ai4040044

Cited by 3 publications

(1 citation statement)

References 78 publications

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“…Microsoft HoloLens 2 is used to share the operating surgeon’s field of view, and accurate 3D annotations are provided with satisfactory accuracy. The application of a telemedicine system for remote patient monitoring is described in [ 11 ], where the use of a teleguided portable ultrasound with ultrasound image analysis was proposed for COVID-19 patients. Using the proposed approach, individuals with limited medical backgrounds can achieve high accuracy in detecting COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation

Šarić,

Russo,

Kraljević

et al. 2023

Sensors

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Recent advances in extended reality (XR) technology have opened the possibility of significantly improving telemedicine systems. This is primarily achieved by transferring 3D information about patient state, which is utilized to create more immersive experiences on VR/AR headsets. In this paper, we propose an XR-based telemedicine collaboration system in which the patient is represented as a 3D avatar in an XR space shared by local and remote clinicians. The proposed system consists of an AR client application running on Microsoft HoloLens 2 used by a local clinician, a VR client application running on the HTC vive Pro used by a remote clinician, and a backend part running on the server. The patient is captured by a camera on the AR side, and the 3D body pose estimation is performed on frames from this camera stream to form a 3D patient avatar. Additionally, the AR and VR sides can interact with the patient avatar via virtual hands, and annotations can be performed on a 3D model. The main contribution of our work is the use of 3D body pose estimation for the creation of a 3D patient avatar. In this way, 3D body reconstruction using depth cameras is avoided, which reduces system complexity and hardware and network resources. Another contribution is the novel architecture of the proposed system, where audio and video streaming are realized using WebRTC protocol. The performance evaluation showed that the proposed system ensures high frame rates for both AR and VR client applications, while the processing latency remains at an acceptable level.

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Section: Related Workmentioning

confidence: 99%

Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation

Šarić,

Russo,

Kraljević

et al. 2023

Sensors

View full text Add to dashboard Cite

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Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review

Kim,

Fischetti,

Guy

et al. 2024

Diagnostics

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Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases—SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.

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Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines

Nekoui,

Seyed Bolouri,

Forouzandeh

et al. 2024

Diagnostics

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Background/Objective: A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where a higher number of B-lines indicates more severe pathology. This paper aimed to evaluate the effectiveness of a newly released lung ultrasound AI tool (ExoLungAI) in the detection of A-lines and quantification/detection of B-lines to help clinicians in assessing pulmonary conditions. Methods: The algorithm is evaluated on 692 lung ultrasound scans collected from 48 patients (65% males, aged: 55 ± 12.9) following their admission to an Intensive Care Unit (ICU) for COVID-19 symptoms, including respiratory failure, pneumonia, and other complications. Results: ExoLungAI achieved a sensitivity of 91% and specificity of 81% for A-line detection. For B-line detection, it attained a sensitivity of 84% and specificity of 86%. In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. Conclusions: ExoLungAI demonstrates a reliable performance in A-line detection and B-line detection/quantification. This automated tool has greater objectivity, consistency, and efficiency compared to manual methods. Many healthcare professionals including intensivists, radiologists, sonographers, medical trainers, and nurse practitioners can benefit from such a tool, as it assists the diagnostic capabilities of lung ultrasound and delivers rapid responses.

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Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound

Cited by 3 publications

References 78 publications

Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation

Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation

Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review

Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines

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