## 7 Conclusions and Future Work  
This study investigated the effectiveness of incorporating SigLIP 2 [17] for feature extraction in a CL/P classification model, building upon our previous work that utilized vision transformers and Siamese neural networks with BiomedCLIP features [2]. Our key finding is that replacing BiomedCLIP with SigLIP 2 significantly improves classification performance across all three CL/P types (bilateral cleft lip and palate, cleft palate only, and unilateral cleft lip and palate) in the UltraSuite CLEFT dataset [3]. The overall accuracy increased from 82.76% to 86.67%, and the improvements in F1 score were statistically significant for all cleft types.  
In direct response to our research question, incorporating SigLIP 2 for feature extraction does indeed improve both the accuracy and efficiency (in terms of classification performance, though not computational time for feature extraction) of CL/P classification compared to the previous ViT-Siamese network model using BiomedCLIP. This improvement is attributed to SigLIP 2's enhanced semantic understanding, multilingual capabilities, and improved training strategy, which result in more robust and discriminative feature representations.  
The broader impact of this work lies in demonstrating the potential of advanced vision-language models, specifically SigLIP 2, to enhance medical image analysis and diagnosis. By leveraging the power of these models, we can achieve more accurate and reliable classification of complex conditions like CL/P, even with limited training data. This contributes to the growing field of AI-powered diagnostics, paving the way for earlier and more personalized interventions in healthcare. The successful application of a multilingual vision-language model also opens up possibilities for broader applicability in diverse clinical settings and patient populations.  
Future research directions are numerous and promising. Exploring different SigLIP 2 variants, particularly those with larger model sizes or those utilizing the NAFlex dynamic resolution capability [17], could potentially lead to further performance gains. Although we demonstrated strong zero-shot performance, fine-tuning SigLIP 2 on the CLEFT dataset, or a larger and more diverse CL/P dataset, is a logical next step that could yield even better results. Investigating the use of SigLIP 2 with other medical imaging datasets beyond CL/P would to help assess its generalizability and potential for broader application in medical diagnostics. Combining SigLIP 2 with other AI models or techniques, such as incorporating clinical metadata or exploring ensemble methods with different architectures, could lead to even more robust and comprehensive diagnostic systems. Finally, developing a user-friendly interface for clinical use is crucial for translating these research findings into practical tools that can benefit clinicians and patients. This could involve integrating the model into existing clinical workflows and providing visualizations and explanations to enhance interpretability and trust.  
Conceptualization, O.N., B.S. and P.P.; methodology, O.N.; software, O.N.; validation, B.S. and P.P.; formal analysis, O.N., B.S. and P.P.; investigation, B.S. and P.P.; resources, O.N.; data curation, O.N.; writing--original draft preparation, O.N.; writing--review and editing, B.S. and P.P.; visualization, O.N.; supervision, B.S. and P.P.; project administration, O.N., B.S. and P.P.; funding acquisition, O.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are contained within the article. The UltraSuite CLEFT dataset is publicly available for research purposes.  
The authors would like to thank the creators of the UltraSuite CLEFT dataset for making this valuable resource publicly available, which facilitated this research.  
The authors declare no conflicts of interest.