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A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion.
Park, Sangjoon; Yuki, Haruhito; Niida, Takayuki; Suzuki, Keishi; Kinoshita, Daisuke; McNulty, Iris; Broersen, Alexander; Dijkstra, Jouke; Lee, Hang; Kakuta, Tsunekazu; Ye, Jong Chul; Jang, Ik-Kyung.
Afiliación
  • Park S; Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
  • Yuki H; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB 800, Boston, MA, 02114, USA.
  • Niida T; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB 800, Boston, MA, 02114, USA.
  • Suzuki K; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB 800, Boston, MA, 02114, USA.
  • Kinoshita D; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB 800, Boston, MA, 02114, USA.
  • McNulty I; Cardiology Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB 800, Boston, MA, 02114, USA.
  • Broersen A; Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, the Netherlands.
  • Dijkstra J; Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, the Netherlands.
  • Lee H; Biostatistics Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA n, USA.
  • Kakuta T; Department of Cardiology, Tsuchiura Kyodo General Hospital, Tsuchiura, Ibaraki, Japan.
  • Ye JC; Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. jong.ye@kaist.ac.kr.
  • Jang IK; Kim Jaechul Graduate School of Artificial Intelligence, Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, 291 Daehak-Ro, Daejeon, 34141, South Korea. jong.ye@kaist.ac.kr.
Sci Rep ; 13(1): 22992, 2023 12 27.
Article en En | MEDLINE | ID: mdl-38151502
ABSTRACT
Patients with acute coronary syndromes caused by plaque erosion might be managed conservatively without stenting. Currently, the diagnosis of plaque erosion requires an invasive imaging procedure. We sought to develop a deep learning (DL) model that enables an accurate diagnosis of plaque erosion using coronary computed tomography angiography (CTA). A total of 532 CTA scans from 395 patients were used to develop a DL model 426 CTA scans from 316 patients for training and internal validation, and 106 separate scans from 79 patients for validation. Momentum Distillation-enhanced Composite Transformer Attention (MD-CTA), a novel DL model that can effectively process the entire set of CTA scans to diagnose plaque erosion, was developed. The novel DL model, compared to the convolution neural network, showed significantly improved AUC (0.899 [0.841-0.957] vs. 0.724 [0.622-0.826]), sensitivity (87.1 [70.2-96.4] vs. 71.0 [52.0-85.8]), and specificity (85.3 [75.3-92.4] vs. 68.0 [56.2-78.3]), respectively, for the patient-level prediction. Similar results were obtained at the slice-level prediction AUC (0.897 [0.890-0.904] vs. 0.757 [0.744-0.770]), sensitivity (82.2 [79.8-84.3] vs. 68.9 [66.2-71.6]), and specificity (80.1 [79.1-81.0] vs. 67.3 [66.3-68.4]), respectively. This newly developed DL model enables an accurate CT diagnosis of plaque erosion, which might enable cardiologists to provide tailored therapy without invasive procedures.Clinical Trial Registration http//www.clinicaltrials.gov , NCT04523194.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de la Arteria Coronaria / Placa Aterosclerótica / Aprendizaje Profundo Límite: Humans Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de la Arteria Coronaria / Placa Aterosclerótica / Aprendizaje Profundo Límite: Humans Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido