Key result
Deep learning segmentation significantly reduced image processing time compared to standard segmentation (3.5 minutes vs 72.3 minutes, p<0.0001) while maintaining high spatial agreement (mean DICE index 0.96).
Why the study?
Artificial intelligence-based deep learning segmentation had not yet been compared against standard segmentation in the context of clinical 3D printed models of the left atrium and appendage.
Does deep learning segmentation improve segmentation time while maintaining accuracy compared to standard radiologist-approved segmentation for left atrial appendage closure planning?
Observational (n=39)
No
Does deep learning segmentation improve segmentation time while maintaining accuracy compared to standard radiologist-approved segmentation for left atrial appendage closure planning?
Absolute Event Rate: 3.5% vs 72.3%
p-value: p=<0.0001
Deep learning-based image segmentation for 3D printed LAA models is highly accurate and significantly faster than standard manual segmentation, facilitating efficient pre-procedural planning.
AI segmentation may accelerate LAA 3D model creation for closure planning; leaves open equivalence to expert segmentation for device sizing.
Background: Medical 3D printing is being increasingly employed for pre-procedural planning and simulation. One important application is in occluder device sizing for left atrial appendage (LAA) closure. Studies have demonstrated clinical utility of 3D printed anatomical models for LAA closure. Artificial intelligence-based segmentation has been applied to multiple cardiovascular diseases, including to LAA segmentation. However, to our knowledge, there has not been a comparison of artificial intelligence-based deep learning segmentation (DLS) where there was a clinical 3D printed model of the left atrium and appendage. Methods: Thirty-nine patients had 3D printed models requested by the interventional cardiologist (IC). Standard segmentation (SS) was performed by a trained engineer and approved by a cardiovascular imager (CI). The models were 3D printed using flexible resin and desktop inverted vat photopolymerization technology. The effort expended throughout the workflow was meticulously documented. Thirty-four of the 39 patients underwent left atrial appendage occlusion using the 3D printed model for device sizing. The 34 patients who underwent a procedure using the 3D printed model were followed for 6 months for major adverse events, device embolization, procedure related myocardial infarction (MI), procedural stroke, new pericardial effusion, pericardial effusion requiring intervention, surgical conversion, and peri-procedural death. All 39 patients also underwent DLS using a commercial software and metrics including segmentation time, segmented volume, DICE index were assessed compared to the SS. A Bland-Altman and regression/correlation analysis was also conducted. Results: The mean SS time was 72.3 minutes whereas the mean DLS time was 3.5 minutes. The DLS closely matched the SS with a mean DICE index of 0.96. The average number of devices attempted was 1.3. The DLS was highly correlated with the SS volume data (r = 0.99). Bland-Altman analysis showed a negative bias (-5.31%) in the volume difference data. There were no long-term complications in the 34 patients who underwent procedure using the 3D printed model for occluder device sizing. Conclusions: We have successfully demonstrated the performance of a commercial DLS algorithm compared to CI approved SS for left atrial appendage occluder device sizing using a clinical 3D printed model.
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Ravi et al. (2022) conducted an observational in Atrial fibrillation with contraindication to oral anticoagulation (n=39). Deep learning segmentation vs. Standard segmentation was evaluated on Segmentation time (p=<0.0001). Deep learning segmentation significantly reduced image processing time compared to standard segmentation (3.5 minutes vs 72.3 minutes, p<0.0001) while maintaining high spatial agreement (mean DICE index 0.96).
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