Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray

• URL: http://arxiv.org/abs/2403.18756v1
• Date: Wed, 27 Mar 2024 16:56:14 GMT
• Title: Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
• Authors: Guglielmo Gallone, Francesco Iodice, Alberto Presta, Davide Tore, Ovidio de Filippo, Michele Visciano, Carlo Alberto Barbano, Alessandro Serafini, Paola Gorrini, Alessandro Bruno, Walter Grosso Marra, James Hughes, Mario Iannaccone, Paolo Fonio, Attilio Fiandrotti, Alessandro Depaoli, Marco Grangetto, Gaetano Maria de Ferrari, Fabrizio D'Ascenzo,
• Abstract summary: Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
• Score: 86.38767955626179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest x-ray with elevated sensitivity, and to predict ASCVD events with elevated negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation.

Related papers

• Multi-modal AI for comprehensive breast cancer prognostication [18.691704371847855]
  We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts. Results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.
  arXiv  Detail & Related papers  (2024-10-28T17:54:29Z)
• Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions [0.8864540224289991]
  Pathologists are facing an increasing workload due to a growing volume of cases and the need for more comprehensive diagnoses. We developed an artificial intelligence (AI) model for triaging cutaneous melanocytic lesions based on whole slide images.
  arXiv  Detail & Related papers  (2024-10-14T13:49:04Z)
• Artificial Intelligence-Based Opportunistic Coronary Calcium Screening in the Veterans Affairs National Healthcare System [0.8655790044415911]
  Coronary artery calcium (CAC) is highly predictive of cardiovascular events. A deep learning algorithm was developed to quantify CAC on non-contrast, non-gated CT scans. Non-gated AI-CAC was predictive of 10-year all-cause mortality.
  arXiv  Detail & Related papers  (2024-09-16T03:59:01Z)
• Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI [6.397650339311053]
  We developed an open-source nnU-Net-based AI model for combined detection and segmentation of unruptured intracranial aneurysms (UICA) in 3D TOF-MRI. Four distinct training datasets were created, and the nnU-Net framework was used for model development. The primary model showed 85% sensitivity and 0.23 FP/case rate, outperforming the ADAM-challenge winner (61%) and a nnU-Net trained on ADAM data (51%) in sensitivity.
  arXiv  Detail & Related papers  (2024-08-30T08:57:04Z)
• Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects [27.786240241494436]
  COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients.
  arXiv  Detail & Related papers  (2023-11-21T18:45:52Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.