PMID- 38496873
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240319
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 10
IP  - 6
DP  - 2024 Mar 30
TI  - Deep learning-based coronary artery calcium score to predict coronary artery 
      disease in type 2 diabetes mellitus.
PG  - e27937
LID - 10.1016/j.heliyon.2024.e27937 [doi]
LID - e27937
AB  - BACKGROUND: Coronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) 
      patients often presents diffuse lesions, with extensive calcification, and it is 
      time-consuming to measure coronary artery calcium score (CACS). OBJECTIVES: To 
      explore the predictive ability of deep learning (DL)-based CACS for obstructive 
      CAD and hemodynamically significant CAD in T2DM. METHODS: 469 T2DM patients 
      suspected of CAD who accepted CACS scan and coronary CT angiography between 
      January 2013 and December 2020 were enrolled. Obstructive CAD was defined as 
      diameter stenosis >/=50%. Hemodynamically significant CAD was defined as CT-derived 
      fractional flow reserve </=0.8. CACS was calculated with a fully automated method 
      based on DL algorithm. Logistic regression was applied to determine the 
      independent predictors. The predictive performance was evaluated with area under 
      receiver operating characteristic curve (AUC). RESULTS: DL-CACS (adjusted odds 
      ratio (OR): 1.005; 95% CI: 1.003-1.006; P ＜ 0.001) was significantly associated 
      with obstructive CAD. DL-CACS (adjusted OR:1.003; 95% CI: 1.002-1.004; P ＜ 0.001) 
      was also an independent predictor for hemodynamically significant CAD. The AUCs, 
      sensitivities, specificities, positive predictive values and negative predictive 
      values of DL-CACS for obstructive CAD and hemodynamically significant CAD were 
      0.753 (95% CI: 0.712-0.792), 75.9%, 66.5%, 74.8%, 67.8% and 0.769 (95% CI: 
      0.728-0.806), 80.7%, 62.1%, 59.6% and 82.3% respectively. It took 1.17 min to 
      perform automated measurement of DL-CACS in total, which was significantly less 
      than manual measurement of 1.73 min (P ＜ 0.001). CONCLUSIONS: DL-CACS, with less 
      time-consuming, can accurately and effectively predict obstructive CAD and 
      hemodynamically significant CAD in T2DM.
CI  - (c) 2024 The Authors.
FAU - Hu, Jingcheng
AU  - Hu J
AD  - Department of Endocrinology, The First Affiliated Hospital of Soochow University, 
      Suzhou, Jiangsu, China.
FAU - Hao, Guangyu
AU  - Hao G
AD  - Department of Radiology, The First Affiliated Hospital of Soochow University, 
      Suzhou, Jiangsu, China.
FAU - Xu, Jialiang
AU  - Xu J
AD  - Department of Cardiology, The First Affiliated Hospital of Soochow University, 
      Suzhou, Jiangsu, China.
FAU - Wang, Ximing
AU  - Wang X
AD  - Department of Radiology, The First Affiliated Hospital of Soochow University, 
      Suzhou, Jiangsu, China.
FAU - Chen, Meng
AU  - Chen M
AD  - Department of Radiology, The First Affiliated Hospital of Soochow University, 
      Suzhou, Jiangsu, China.
LA  - eng
PT  - Journal Article
DEP - 20240310
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10944251
OTO - NOTNLM
OT  - Coronary artery calcium score
OT  - Coronary artery disease
OT  - Deep learning
OT  - Prediction
OT  - Type 2 diabetes mellitus
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2024/03/18 06:42
MHDA- 2024/03/18 06:43
PMCR- 2024/03/10
CRDT- 2024/03/18 04:44
PHST- 2023/07/05 00:00 [received]
PHST- 2024/03/03 00:00 [revised]
PHST- 2024/03/08 00:00 [accepted]
PHST- 2024/03/18 06:43 [medline]
PHST- 2024/03/18 06:42 [pubmed]
PHST- 2024/03/18 04:44 [entrez]
PHST- 2024/03/10 00:00 [pmc-release]
AID - S2405-8440(24)03968-9 [pii]
AID - e27937 [pii]
AID - 10.1016/j.heliyon.2024.e27937 [doi]
PST - epublish
SO  - Heliyon. 2024 Mar 10;10(6):e27937. doi: 10.1016/j.heliyon.2024.e27937. 
      eCollection 2024 Mar 30.