Comparative Eminence: Foundation versus Domain-Specific Model for Cardiac Ultrasound Segmentation

Importance A recently developed vision foundation model, “Segment Anything (SAM),” promises to segment any objects in images. However, the performance of SAM on clinical echocardiography images is yet to be investigated and compared against the domain-specific models. Objective To evaluate the performance of SAM on transthoracic echocardiography (TTE) and point-of-care ultrasound (POCUS) images. Design SAM was fine-tuned on the training set of EchoNet-Dynamic (TTE) and then evaluated on datasets containing TTE and POCUS images. Setting Multi-center, retrospective cohort study. Participants This study used two publicly available datasets (EchoNet-dynamic, Stanford University and CAMUS, University Hospital of St Etienne). The Mayo Clinic dataset contains a sample of 99 non-duplicated patients (58 TTE and 41 POCUS). Intervention/Exposure not applicable. Main Outcomes and Measures Model segmentation performance: Dice similarity coefficient (DSC). Results Fine-tuned SAM had promising frame-level performance (SAM vs. EchoNet: DSC 0.911 ± 0.045 vs. 0.915 ± 0.047, p<0.0001), and consistent performance on the external datasets including TTE (Mayo Clinic: DSC 0.902 ± 0.032 vs. 0.893 ± 0.090, p<0.0001, CAMUS-A4C: DSC 0.897 ± 0.036 vs. 0.850 ± 0.097, p<0.0001, CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p<0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p<0.0001). Conclusions and Relevance Promising segmentation performance was observed after fine-tuning the SAM model on TTE. The strong generalization capability of SAM can facilitate the development of AI applications in cardiac ultrasound with less manual data curation. Question What is the comparative performance of fine-tuned Segment Anything Model (SAM) against domain-specific segmentation model on transthoracic echocardiography (TTE) and point-of-care ultrasound (POCUS)? Findings Fine-tuned SAM had excellent performance on EchoNet dataset (SAM vs. EchoNet: DSC 0.911 ± 0.045 vs. 0.915 ± 0.047, p<0.0001) and generalized well on external datasets containing TTE (Mayo TTE: DSC 0.902 ± 0.032 vs. 0.893 ± 0.090, p<0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p<0.0001). Meaning The generalization capability of SAM can facilitate the development of AI applications in echocardiography and POCUS with minimal expert data curation. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Mayo Clinic gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript * AI : Artificial intelligence A2C : apical 2 chamber, echocardiography view A4C : apical 4 chamber, echocardiography view CAMUS : Cardiac Acquisitions for Multi-structure Ultrasound Segmentation IoU : Intersection over the union DSC : Dice similarity coefficient LV : Left ventricle POCUS : Point-of-care ultrasound SAM : Segment anything model TTE : Transthoracic echocardiography ViT : Vision Transformer