## 2 Related Works  
Our previous work [2] established a foundation for cleft lip and/or palate (CL/P) classification using artificial intelligence, specifically employing vision transformers (ViTs) and Siamese neural networks. This approach was informed by several key studies. [PERSON] et al. [10] developed a deep learning model combining LSTM and DRNN for hypernasality detection in Mandarin-speaking patients with CL/P, achieving high accuracy, albeit focusing solely on speech audio data. [PERSON] et al. [11] utilized a CNN framework (U-net and Dense U-net) for automatic tongue contour tracking in ultrasound images, demon strating the potential of deep learning for anatomical analysis in CL/P. [PERSON] et al. [12] explored articulatory-to-acoustic mapping using ultrasound images and residual networks, highlighting the feasibility of processing different ultrasound image representations. [PERSON] et al. [13] compared various segmentation techniques for tongue edge detection in ultrasound images, finding CNNs and U-nets superior to traditional methods. These studies, along with others focusing on speech assessment [14] and the psychological aspects of CL/P [15; 16], underscored the need for a multimodal approach integrating both anatomical and functional information while also addressing the challenge of limited data availability in medical imaging [22]. Our previous work addressed these needs by combining ViTs and Siamese networks, achieving competitive results with few-shot learning on multimodal data [2; 4; 5]. Since the publication of our previous work [2], the field of vision-language models has advanced significantly. The development of SigLIP [18] and subsequently SigLIP 2 [17] represents a major step forward. SigLIP, introduced by [PERSON] et al. [18], proposed a novel sigmoid loss function for language-image pre-training, improving upon the contrastive loss used in models like CLIP [19]. This resulted in stronger performance on various downstream tasks. SigLIP 2 [17; 20; 21] further enhanced this approach, with improved training strategies, provided larger model sizes, and, crucially, provided multilingual support. This multilingual capability is particularly relevant to CL/P research, as it allows for the analysis of speech data from diverse linguistic backgrounds, broadening the applicability of AI-powered diagnostic tools. While BiomedCLIP [6] demonstrated the effectiveness of adapting vision-language models to the biomedical domain, its focus on English-language text limits its utility in multilingual contexts [7; 8; 9]. SigLIP 2's architecture and training methodology enable it to capture more nuanced semantic relationships and finer-grained visual details, making it a promising alternative for medical image analysis. The application of vision-language models in medical imaging is a rapidly growing area of research. While direct applications of SigLIP/SigLIP 2 to CL/P are still emerging, related work demonstrates the potential of these models in other medical domains. For example, studies have explored the use of vision-language models for tasks such as medical report generation [7], disease classification from medical images [8], and visual question answering in radiology [9]. These studies highlight the ability of vision-language models to leverage both visual and textual information for improved understanding and analysis of medical data. Few-shot learning remains a critical area of research in medical imaging, given the inherent challenges in obtaining large, labeled datasets. Recent work has explored various techniques for improving few-shot learning performance, including meta-learning approaches [4], data augmentation strategies specifically designed for medical images [5], and the use of self-supervised learning to pre-train models on unlabeled data [22]. These advancements are relevant to CL/P classification, as they offer potential avenues for further enhancing the performance of models like ours, which rely on Siamese networks for few-shot learning.