NDLI: A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography

Content Provider	Springer Nature : BioMed Central
Author	Ajmera, Pranav Kharat, Amit Seth, Jitesh Rathi, Snehal Pant, Richa Gawali, Manish Kulkarni, Viraj Maramraju, Ragamayi Kedia, Isha Botchu, Rajesh Khaladkar, Sanjay
Abstract	Background Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and mortality. However, PE remains subject to misdiagnosis. Methods We retrospectively identified 251 CTPAs performed at a tertiary care hospital between January 2018 to January 2021. The scans were classified as positive (n = 55) and negative (n = 196) for PE based on the annotations made by board-certified radiologists. A fully anonymized CT slice served as input for the detection of PE by the 2D segmentation model comprising U-Net architecture with Xception encoder. The diagnostic performance of the model was calculated at both the scan and the slice levels. Results The model correctly identified 44 out of 55 scans as positive for PE and 146 out of 196 scans as negative for PE with a sensitivity of 0.80 [95% CI 0.68, 0.89], a specificity of 0.74 [95% CI 0.68, 0.80], and an accuracy of 0.76 [95% CI 0.70, 0.81]. On slice level, 4817 out of 5183 slices were marked as positive for the presence of emboli with a specificity of 0.89 [95% CI 0.88, 0.89], a sensitivity of 0.93 [95% CI 0.92, 0.94], and an accuracy of 0.89 [95% CI 0.887, 0.890]. The model also achieved an AUROC of 0.85 [0.78, 0.90] and 0.94 [0.936, 0.941] at scan level and slice level, respectively for the detection of PE. Conclusion The development of an AI model and its use for the identification of pulmonary embolism will support healthcare workers by reducing the rate of missed findings and minimizing the time required to screen the scans.
Related Links	https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-022-00916-0.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14712342
DOI	10.1186/s12880-022-00916-0
Journal	BMC Medical Imaging
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2022-11-11
Access Restriction	Open
Subject Keyword	Imaging Radiology Artificial intelligence Pulmonary embolism Computed tomographic pulmonary angiography U-Net architecture
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	2.8/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automated detection of pulmonary embolism from CT-angiograms using deep learning

Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images.

Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography.

Current Role of Multi-detector Computed Tomography (MDCT) in Diagnosis of Pulmonary Embolism

How artificial intelligence improves radiological interpretation in suspected pulmonary embolism.

Use of a Deep Learning Algorithm for Detection and Triage of Cancer-associated Incidental Pulmonary Embolism.

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.

Validation of an automated technique for quantification of pulmonary perfusion territories using computed tomography angiography.

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography

Similar Documents

Automated detection of pulmonary embolism from CT-angiograms using deep learning

Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images.

Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography.

Current Role of Multi-detector Computed Tomography (MDCT) in Diagnosis of Pulmonary Embolism

How artificial intelligence improves radiological interpretation in suspected pulmonary embolism.

Use of a Deep Learning Algorithm for Detection and Triage of Cancer-associated Incidental Pulmonary Embolism.

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.

Validation of an automated technique for quantification of pulmonary perfusion territories using computed tomography angiography.

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography