TY - JOUR
T1 - AI-Based Algorithm to Detect Heart and Lung Disease From Acute Chest Computed Tomography Scans
T2 - Protocol for an Algorithm Development and Validation Study
AU - Olesen, Anne Sophie Overgaard
AU - Miger, Kristina
AU - Ørting, Silas Nyboe
AU - Petersen, Jens
AU - de Bruijne, Marleen
AU - Boesen, Mikael Ploug
AU - Andersen, Michael Brun
AU - Grand, Johannes
AU - Thune, Jens Jakob
AU - Nielsen, Olav Wendelboe
N1 - ©Anne Sophie Overgaard Olesen, Kristina Miger, Silas Nyboe Ørting, Jens Petersen, Marleen de Bruijne, Mikael Ploug Boesen, Michael Brun Andersen, Johannes Grand, Jens Jakob Thune, Olav Wendelboe Nielsen. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 19.09.2025.
PY - 2025/9/19
Y1 - 2025/9/19
N2 - BACKGROUND: Dyspnea is a common cause of hospitalization, posing diagnostic challenges among older adult patients with multimorbid conditions. Chest computed tomography (CT) scans are increasingly used in patients with dyspnea and offer superior diagnostic accuracy over chest radiographs but face limited use due to a shortage of radiologists.OBJECTIVE: This study aims to develop and validate artificial intelligence (AI) algorithms to enable automatic analysis of acute CT scans and provide immediate feedback on the likelihood of pneumonia, pulmonary embolism, and cardiac decompensation. This protocol will focus on cardiac decompensation.METHODS: We designed a retrospective method development and validation study. This study has been approved by the Danish National Committee on Health Research Ethics (1575037). We extracted 4672 acute chest CT scans with corresponding radiological reports from the Copenhagen University Hospital-Bispebjerg and Frederiksberg, Denmark, from 2016 to 2021. The scans will be randomly split into training (2/3) and internal validation (1/3) sets. Development of the AI algorithm involves parameter tuning and feature selection using cross validation. Internal validation uses radiological reports as the ground truth, with algorithm-specific thresholds based on true positive and negative rates of 90% or greater for heart and lung diseases. The AI models will be validated in low-dose chest CT scans from consecutive patients admitted with acute dyspnea and in coronary CT angiography scans from patients with acute coronary syndrome.RESULTS: As of August 2025, CT data extraction has been completed. Algorithm development, including image segmentation and natural language processing, is ongoing. However, for pulmonary congestion, the algorithm development has been completed. Internal and external validation are planned, with overall validation expected to conclude in 2025 and the final results to be available in 2026.CONCLUSIONS: The results are expected to enhance clinical decision-making by providing immediate, AI-driven insights from CT scans, which will be beneficial for both clinicians and patients.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77030.
AB - BACKGROUND: Dyspnea is a common cause of hospitalization, posing diagnostic challenges among older adult patients with multimorbid conditions. Chest computed tomography (CT) scans are increasingly used in patients with dyspnea and offer superior diagnostic accuracy over chest radiographs but face limited use due to a shortage of radiologists.OBJECTIVE: This study aims to develop and validate artificial intelligence (AI) algorithms to enable automatic analysis of acute CT scans and provide immediate feedback on the likelihood of pneumonia, pulmonary embolism, and cardiac decompensation. This protocol will focus on cardiac decompensation.METHODS: We designed a retrospective method development and validation study. This study has been approved by the Danish National Committee on Health Research Ethics (1575037). We extracted 4672 acute chest CT scans with corresponding radiological reports from the Copenhagen University Hospital-Bispebjerg and Frederiksberg, Denmark, from 2016 to 2021. The scans will be randomly split into training (2/3) and internal validation (1/3) sets. Development of the AI algorithm involves parameter tuning and feature selection using cross validation. Internal validation uses radiological reports as the ground truth, with algorithm-specific thresholds based on true positive and negative rates of 90% or greater for heart and lung diseases. The AI models will be validated in low-dose chest CT scans from consecutive patients admitted with acute dyspnea and in coronary CT angiography scans from patients with acute coronary syndrome.RESULTS: As of August 2025, CT data extraction has been completed. Algorithm development, including image segmentation and natural language processing, is ongoing. However, for pulmonary congestion, the algorithm development has been completed. Internal and external validation are planned, with overall validation expected to conclude in 2025 and the final results to be available in 2026.CONCLUSIONS: The results are expected to enhance clinical decision-making by providing immediate, AI-driven insights from CT scans, which will be beneficial for both clinicians and patients.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77030.
KW - Humans
KW - Tomography, X-Ray Computed/methods
KW - Algorithms
KW - Lung Diseases/diagnostic imaging
KW - Retrospective Studies
KW - Artificial Intelligence
KW - Denmark
KW - Heart Diseases/diagnostic imaging
KW - Radiography, Thoracic/methods
KW - Pulmonary Embolism/diagnostic imaging
KW - Dyspnea/diagnostic imaging
U2 - 10.2196/77030
DO - 10.2196/77030
M3 - Journal article
C2 - 40973115
SN - 1929-0748
VL - 14
JO - JMIR research protocols
JF - JMIR research protocols
M1 - e77030
ER -