This content originally appeared on HackerNoon and was authored by Tomography
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Abstract
Purpose: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using Computed Tomography Pulmonary Angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE mortality.
\ Materials and Methods: 918 patients (median age 64 years, range 13-99 years, 52% female) with 3,978 CTPAs were identified via retrospective review across three institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and/or clinical variables were then incorporated into DL models to predict survival outcomes. Four models were developed as follows: (1) using CTPA imaging features only; (2) using clinical variables only; (3) multimodal, integrating both CTPA and clinical variables; and (4) multimodal fused with calculated PESI score. Performance and contribution from each modality were evaluated using concordance index (c-index) and Net Reclassification Improvement, respectively. Performance was compared to PESI predictions using the Wilcoxon signed-rank test. Kaplan-Meier analysis was performed to stratify patients into high- and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction.
\ Results: For both data sets, the PESI-fused and multimodal models achieved higher c-indices than PESI alone. Following stratification of patients into high- and low-risk groups by multimodal and PESI-fused models, mortality outcomes differed significantly (both p<0.001). A strong correlation was found between high-risk grouping and RV dysfunction.
\ Conclusions: Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.
Abbreviations
AI = Artificial Intelligence
\ c-index = Concordance Index
\ CoxPH = Cox Proportional Hazards
\ CTPA = Computed Tomography Pulmonary Angiography
\ DL = Deep Learning
\ PE = Pulmonary Embolism
\ PESI = Pulmonary Embolism Severity Index
\ RSF = Random Survival Forest
\ RV = Right Ventricular
\ TTE, TEE = Transthoracic, Transesophageal Echocardiography
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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:::info Authors:
(1) Zhusi Zhong, BS, a Co-first authors from Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA, Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA, and School of Electronic Engineering, Xidian University, Xi’an 710071, China;
(2) Helen Zhang, BS, a Co-first authors from Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;
(3) Fayez H. Fayad, BA, a Co-first authors from Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;
(4) Andrew C. Lancaster, BS, Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA and Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA;
(5) John Sollee, BS, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;
(6) Shreyas Kulkarni, BS, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;
(7) Cheng Ting Lin, MD, Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA;
(8) Jie Li, PhD, School of Electronic Engineering, Xidian University, Xi’an 710071, China;
(9) Xinbo Gao, PhD, School of Electronic Engineering, Xidian University, Xi’an 710071, China;
(10) Scott Collins, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;
(11) Colin Greineder, MD, Department of Pharmacology, Medical School, University of Michigan, Ann Arbor, MI, 48109, USA;
(12) Sun H. Ahn, MD, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;
(13) Harrison X. Bai, MD, Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA;
(14) Zhicheng Jiao, PhD, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;
(15) Michael K. Atalay, MD, PhD, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA.
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This content originally appeared on HackerNoon and was authored by Tomography
Tomography | Sciencx (2025-10-02T17:00:03+00:00) Predicting PE Mortality: Enhancing Clinical Risk Assessment with AI and Multimodal Deep Learning. Retrieved from https://www.scien.cx/2025/10/02/predicting-pe-mortality-enhancing-clinical-risk-assessment-with-ai-and-multimodal-deep-learning/
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