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Advanced Ultrasound in Diagnosis and Therapy ›› 2023, Vol. 7 ›› Issue (3): 217-227.doi: 10.37015/AUDT.2023.230030

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  • 收稿日期:2023-04-08 修回日期:2023-04-16 接受日期:2023-07-27 出版日期:2023-09-30 发布日期:2023-10-09

Artificial Intelligence-assisted Medical Imaging in Interventional Management of Valvular Heart Disease

Wenwen Chen, BSa,b,c, Yuji Xie, MDa,b,c, Zisang Zhang, MDa,b,c, Ye Zhu, MSa,b,c, Yiwei Zhang, MDa,b,c, Shuangshuang Zhu, MD, PhDa,b,c, Chun Wu, MD, PhDa,b,c, Ziming Zhang, MDa,b,c, Xin Yang, PhDa,b,c, Man wei Liu, MD, PhDa,b,c, Mingxing Xie, MD, PhDa,b,c,*(), Li Zhang, MD, PhDa,b,c,*()   

  1. a Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    b Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China
    c Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
  • Received:2023-04-08 Revised:2023-04-16 Accepted:2023-07-27 Online:2023-09-30 Published:2023-10-09
  • Contact: Mingxing Xie, MD, PhD, Li Zhang, MD, PhD E-mail:xiemx@hust.edu.cn;zli429@hust.edu.cn

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Abstract:

The integration of medical imaging and artificial intelligence (AI) has revolutionized interventional therapy of valvular heart diseases (VHD), owing to rapid development in multimodality imaging and healthcare big data. Medical imaging techniques, such as echocardiography, cardiovascular magnetic resonance (CMR) and computed tomography (CT), play an irreplaceable role in the whole process of pre-, intra- and post-procedural intervention of VHD. Different imaging techniques have unique advantages in different stages of interventional therapy. Therefore, single imaging technique can’t fully meet the requirements of complicated clinical scenarios. More importantly, a single intraoperative image provides only limited vision of the surgical field, which could be a potential source for unsatisfactory prognosis. Besides, the non-negligible inter- and intra-observer variability limits the precise quantification of heart valve structure and function in daily clinical practice. With the help of analysis clustered and regressed by big data and exponential growth in computing power, AI broken grounds in the interventional therapy of VHD, including preoperative planning, intraoperative navigation, and postoperative follow-up. This article reviews the state-of-the-art progress and directions in the application of AI for medical imaging in the interventional therapy of VHD.

Key words: VHD, AI, Machine learning, Medical imaging

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Study VHD Task AI methods Samples Performance metrics
Kang, N. G. et al. [4](2021) AS Diagnosed severe AS LASSO; RFs; XGBoost Training: 312 subjects Testing: 96 subjects AUC: 0.921 (LASSO & XGBoost)
Sengupta, P. P. et al. [5](2021) AS Distinguished AS phenotypes TDA; ML 1964 subjects AUC: 0.988
Accuracy (%): 94.3
Precision (%): 91.3
Recall (%): 95.5
Yang, F. et al. [6](2014) AS Differential diagnosis with other diseases ML Training: 1335 subjects Validation: 311 subjects
Testing: 434 subjects		 AUC: 0.97
Accuracy (%): 94
Sensitivity (%): 90
Specificity (%): 94
Moghaddasi, H. et al. [7](2016) MR Detected normal, mild, moderate and severe MR subjects Textural analysis; SVM;
LDA; TM		 5004 images Accuracy (%): 99.45 (SVM) Accuracy (%): 95.72 (LDA,NN) Accuracy (%): 95 (TM)
Sensitivity (%): 99.38
Specificity (%): 99.63
Pimor, A. et al. [8](2019) MR Distinguished MR phenotypes DL 122 subjects HR: 3.57 (1.72-7.44)
Bartko, P. E. et al. [9](2021) MR Explored the morphological and Functional characteristics of secondary MR PC; cluster analysis 383 subjects HR: 2.18 (clusters3)

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Study Operation Task Technology Samples Performance metrics
Theriault-Lauzier, P. et al. [10] (2021) TAVR Plane positioning CNN 94 subjects Localization error (mm): 0.9 ± 0.8 (testing)
Al, W.A. et al. [11] (2018) TAVR Located important anatomic markers ML 71 subjects Localization error (mm): 2.04 ± 1.11
Rocatello, G. et al. [12] (2019) TAVR Determined the optimal valve size and implantation position FE 62 subjects Accuracy (%): 71
Maximum contact pressure (%): 75
Contact pressure index (%): 71
De Jaegere, P. et al. [13] (2016) TAVR Simulated the TAVR surgical process FE 60 subjects Accuracy (%): 80
Cutoff value (ml/s): 16.0
Sensitivity: 0.72
Specificity: 0.78
Auricchio, F. et al. [14] (2014) TAVR Simulated valve implantation FE 2 subjects Stress state has consistency
(between 2 subjects)
Astudillo, P. et al. [15] (2019) TAVR Calculated the size of the implanted prosthesis CNN Training: 355subjects
Testing:
118 subjects		 Total analysis time(s): < 1
Device size has consistency
(between the manual and automatic selection)
Astudillo, P. et al. [16] (2019) TMVR Measured multiple biological parameters DL 71 subjects Total analysis time (s): < 1
Oguz, D. et al. [17] (2019) TMVR Explored the correlation between 3D-TEE parameters and MR reduction 3D TEE; Mitral Valve Navigator. 59 subjects Optimal MR reduction: 68%
Guerrero, M. et al. [18] (2018) TMVR Simulated valve implantation FE / Total analysis time: < 3 h
Wang, D.D. et al. [19,20] (2018) TMVR Simulated valve implantation CAD 38 subjects R2: 0.8169 (neo-LVOT surface area)
Sensitivity: 100%
Specificity: 96.8%
Kong, F. et al. [21] (2020) TEER simulated the biomechanics of MC implantation FE; MC. 1 subject Antero-posterior distance: ↓26%
Annulus area: ↓19%
Valve opening orifice area: ↓48%
Regurgitant orifice area: ↓63%
Anterior leaflet peak stresses: ↑ 64%
Posterior leaflet peak stresses: ↑62%
Anterior leaflet peak strains: ↑ 20%
Posterior leaflet peak strains: ↑10%
Sturla, F. et al. [22] (2015) TEER Simulated the biomechanics of MC implantation FE; MC. 3 subjects Systolic CoA: ↑11-40%
Systolic leaflet stresses (Kpa): 100-500
Diastolic leaflet stresses (Kpa): 250
(subject 3) Diastolic orifice area (%): ↓58.9%
Caballero, A. et al. [23] (2020) TEER Evaluated the biomechanics of MC implantation FE; FSI; MC. 1 subject Antero-posterior distance: ↓28%
Mitral annulus spherecity index: ↓39%
Anatomic regurgitant orifice area: ↓52% Anatomic opening orifice area: ↓71%
Diastolic anterior leaflet stress: ↑210%
Diastolic posterior leaflet stress: ↑145%
Mansi, T. et al. [24] (2012) TEER Evaluated the biomechanical impact of mitral valve repair FE; ML. 25 subjects Mean error (mm):1.49 ± 0.62 (ground truth)
Mean error (mm):2.75 ± 0.86 (automatic detection)
Total analysis time (min): < 14
Dabiri, Y. et al. [25] (2023) TEER Predicted the effect of TEER therapy with MC DL; XGBoost. 1267 FE models MAPE: 54 and 0.310 (DL)
MAPE: 0.115 and 0.231 (XGBoost)
Total analysis time (s): < 1

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Study Operation Task Technology Samples Performance metrics
Biaggi, P. et al. [26] (2020) TAVR Investigated the efficacy of FS in the perioperative period of TAVR FI; EN; 3D TEE. Total: 138subjects FS+: 69subjects FS-: 69subjects Procedure time (min): 42.1 ± 15.2 (FS+)
Procedure time (min): 49.2 ± 20.7 (FS-)
Contrast agent use (ml): 34.3 ± 22.0 (FS+)
Contrast agent use (ml): 39.0 ± 23.3 (FS-)
Fluoroscopy time (min): 11.4 ± 4.7 (FS+)
Fluoroscopy time (min): 10.9 ± 5.5 (FS-)
Pearson correlation r: 0.63-0.78
Interclass correlation coefficient: 0.95-0.99
Luo, Z. et al. [27] (2013) TAVR Reconstructed aortic valve models and determined the target location for aortic valve prosthesis implantation MTS; 2D US; 4D CT. ECG signal Aortic root segmentation algorithm error (mm): 0.92 ± 0.85
Computational time (ms): 36.13 ± 6.26
Yielding fiducial localization errors (mm):
3.02 ± 0.39
Target registration errors(mm): 3.31 ± 1.55
Deployment distance(mm): 3.23 ± 0.94
Tilting errors (°): 5.85 ± 3.06
Mazomenos, E. B. et al. [28] (2016) TAVR Evaluated surgical skills and verified the role of robot assisted TAVR surgery FE; k-means clustering; EM. 12 subjects (novice group: 6 subjects) The median value of the procedure time (s): 34.9 (stage 1)
The median value of the procedure time (s): 111.2 (stage 2)
Maximum accuracy (%): 83 (k-means)
Maximum accuracy (%): 91 (EM)
Average speed (px/s): 22.3 (stage1)
Average speed (px/s): 22 (stage2)
P = 0.031(conventional equipment vs robotic system )
Prihadi, E. A. et al. [29] (2018) TAVR Quantified aortic ring and root size 3D TEE; AVN. 150 subjects Mean analysis time (min): 4.2 ± 1.0
r≥0.90 (inter- and intra-observer variability)
Lang, P. et al. [30] (2012) TAVR Build TAVI's enhanced image guidance system 3D TEE / Mean contour boundary distance error (mm): 1.3 (short-axis views)
Mean contour boundary distance error (mm): 2.8 (long-axis views)
Mean target registration error (mm): 5.9
Coisne, A. et al. [31] (2020) TMVR Defined the optimal 3D TEE parameters for TMVR 3D TEE 57 subjects AUC: 0.88-0.91 (mitral annular area)
AUC: 0.85-0.91 (mitral annular perimeter)
Jin, C. N. et al. [32] (2016) TMVR Located MVP AIUS 90 subjects Accuracy (%): 89 (nonexperts)
Image analysis time (min): 1.9 ± 0.7 (experts)
Image analysis time (min): 5.0 ± 0.5 (nonexperts)
Altiok, E. et al. [33] (2011) TEER Evaluated the value of RT 3D TEE RT 3D TEE; 2D TEE. 28 subjects Advantages: 9/11 (RT 3D TEE)
Melillo, F. et al. [34] (2021) TEER Explored the TEER treatment effect after MC implantation FI; MC. 80 subjects Fluoroscopy time (min): 37.3 ± 14.6
Procedural time (min): 92.2 ± 36.1
Sündermann, S.H. et al. [35] (2014) TEER Evaluated the feasibility and safety of using MC EN Software; MC. 21 subjects Radiation dose (Gy/cm2): 146.5 ± 123.6
Total procedure time (min): 136.2 ± 50.2
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