## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Quantitative Imaging and Prognosis
Even with limited data, the precision achieved by this approach was comparable to that of manual segmentation conducted by experienced operators. High agreement is observed between manual and automatic segmentation methods, with intraclass correlation coefficients (ICC) exceeding 0.964 on 217 images. Furthermore, excellent reproducibility is demonstrated by the automatic method, with an ICC greater than 0.913. These results highlight the effectiveness of deep learning in accurately and consistently segmenting choroidal boundaries for the analysis of CT and vasculature. The impact of accurate choroidal segmentation using deep learning on personalized medicine concept is substantial, as it contributes to early disease detection, more precise diagnoses, better disease progression monitoring, and overall improvements in the efficiency of ocular healthcare. In ophthalmology, understanding the human vitreous structure is vital, given its substantial age-related variations, but in vivo study limitations have persisted due to the vitreous transparency and mobility. Although OCT is routinely used to identify boundaries within the vitreous, the acquisition of high-resolution images suitable for generating 3D representations remains a challenge. A study used ML-based 3D modeling, employing a CNN network trained on manually labeled fluid areas [100]. The trained network automatically labeled vitreous fluid, generating 3D models and quantifying vitreous fluidic cavities. This innovative modeling system introduced novel imaging markers with the potential to advance our understanding of the aging processes and the diagnosis of various eye diseases, contributing significantly to ocular health assessment and clinical management. For precise eye disorder quantification, particularly in complex cases with distorted anatomy, automated segmentation of fluid spaces in OCT imaging is crucial. A novel end-to-end ML approach was presented for combining a random forest classifier for accurate fluid detection and an efficient DeepLab algorithm for quantification and labeling [101]. The method achieves an average Dice score of 86.23%, compared to manual delineations by an expert. This approach promises to significantly improve automated fluid space segmentation and quantification in OCT imaging, enhancing clinical management and monitoring of eye disorders, particularly in complex cases. Accurate quantification of intrachoroidal cavitations (ICCs) and their effect on visual function is paramount, particularly in high myopia. A study introduced a new 3D volume parameter for ICCs, addressing the need for precise quantification [102]. A significant knowledge gap exists regarding the relationship between 3D ICC volume and visual field sensitivity, and this study quantifies this correlation. Through deep learning-based noise reduction, the study quantified ICCs in 13 eyes with high myopia. It revealed negative correlations between ICC volume, length, depth, and visual field metrics, highlighting the role of quantification in understanding ICC impact. This research introduces a novel parameter for ICC assessment, enhancing our understanding of their effect on visual function. This has the potential to improve clinical detection and precise quantification of ICC pathology in high myopia, ultimately benefiting patient care and management. Another success of ML models in OCT is enabling quantification of cellular microstructures that are finer than the inherent resolution of OCT. Alterations in light scattering patterns stemming from distributed particles within the phantoms yield significant optical signals. ML has proven to be successful in quantifying such non-trivial patterns in the OCT results. In a study, OCT images of assorted tissue-mimicking phantoms underwent analysis via SVM models to quantify speckle through texture [103].