# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
## 5 Conclusions  
This research presents an automated system for classifying ST structures. The proposed method employs an SSL framework and has undergone evaluation through five-fold cross-validation. The validation process utilizes both the author's and an external dataset from IEEE ISBI 2015. In binary classification, the SSLDSE approach demonstrates an average accuracy of 96.70%, with average recall, precision, and F1 scores of 96.69%, 97.59%, and 97.11%, respectively. To the researcher's knowledge, this study is the first to propose an automated ST binary classification method that incorporates SSL techniques with enhanced modules. The researchers acknowledge certain constraints in current state-of-the-art methods. First, the critical number of samples is dynamically variable and complex, bridging ST structures. Additionally, available X-ray datasets suffer from artifacts and contain a significant amount of unlabeled data, leading to datasets lacking robustness. However, the existing empirical evidence on SSL applications indicates that performance improves as the number of high-quality training samples increases. Furthermore, it is crucial to thoroughly evaluate such methods, taking into account the diverse ST structures found in different patients. While the proposed approach shows potential in classifying the advanced stages of ST, it is important to focus on improving performance in the initial stages, where the results may be less optimal. This study has significant implications for orthodontics and medical imaging. Automating ST classification offers critical insights into craniofacial growth patterns and anomalies, thereby aiding in early diagnosis and treatment planning while reducing reliance on manual assessments. Moreover, the SSLDSE framework illustrates the potential of SSL to effectively utilize unlabeled data, providing a robust solution for challenging tasks in medical imaging. Future work will extend this framework to include the multiclass classification of ST subtypes and apply it across diverse imaging modalities. This advancement could pave the way for the integration of machine learning tools into clinical practice, ultimately enhancing patient care.  
**Author Contributions:** Conceptualization, K.S.S., A.A., J.P. and A.L.; methodology, K.S.S., A.A. and J.P.; validation, A.A., J.P., M.J. and A.L.; formal analysis, V.K. and P.K.; investigation, K.S.S., A.A., J.P. and A.L.; resources, A.A., J.P., M.J. and A.L.; data curation, K.S.S., M.J. and V.K.; writing--original draft preparation, K.S.S. and P.K.; writing--review and editing, K.S.S., A.A., J.P., P.K. and A.L.; supervision, A.A., J.P., M.J., A.L. and V.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was approved by the Ethics Committee of the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India (Registration No: PGI/IEC/2021/001488 and IEC No: IEC-09/2021-2119) on 19 October 2021.  
**Informed Consent Statement:** Informed consent for participation was obtained by the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India, as per their standard procedure.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy regulation.  
**Acknowledgments:** The authors would like to express their gratitude to the Oral Health Sciences Centre at the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India, for providing cephalometric X-ray data under ethical clearance number IEC-09/2021-2119. They also thank the faculty of the CSIR--Central Scientific Instruments Organisation (CSIO), Chandigarh, India, and the School of Science at RMIT University, Melbourne, Australia, for their valuable assistance.  
**Conflicts of Interest:** The authors declare no conflicts of interest.