## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Quantitative Imaging and Prognosis  
ML has also proven to be powerful in quantifying signals obtained through SWIR techniques, offering insights into health-related measurements. For instance, the quantification of water and lipid contents in tissues holds value for monitoring physiologic levels. To this end, an SWIR probe designed for scanning thin tissues, utilizing diffused light, was constructed using three LED light sources and four source-detector separations [73]. Employing a DNN, the percentage of water and lipid components were estimated by analyzing the received signals in the detectors. Training the DNN involved simulating various conditions through precise Monte Carlo simulations. Results on phantoms demonstrated accurate quantification of water (2.1 \(\pm\) 1.1% error) and lipid (1.2 \(\pm\) 1.5% error) components using this approach. Expanding this study to the meso-patterned imaging (MPI) modality facilitated monitoring important physiological processes in clinics, including edema, inflammation, and tumor lipid heterogeneity [75]. In this context, hyperspectral MPI results were subjected to analysis by an SVM model for identifying subcutaneous brown adipose tissue. In another study utilizing spectroscopy, in vivo skin parameters were quantified by applying two ML models to spectral reflectance results captured by sensors spanning from 450 to 1800 nm [74]. Quantifying skin parameters holds immense importance in dermatology, benefiting cancer diagnostics, wound healing, drug delivery, and related applications such as skin aging. The study introduced a theoretical model correlating skin physiological parameters to back-reflected light spectra. SVM and KNN models were then applied in a reverse modeling approach to derive skin parameters from the back-reflected spectra. Results from 24 human cases showcased favorable agreement between the prediction results from this non-invasive method and ground truth data. Within the domain of OCT, quantitative techniques have emerged, which are independent of the specific OCT system used for data acquisition for clinically relevant measurements. ML models have also proven to be capable in enhancing precision in quantification of OCT results. For instance, in cardiology, quantifying lipid content aids clinicians in determining the growth stage of atherosclerotic plaques. Beyond cap thickness, lipid-rich necrotic cores serve as significant indicators of high-risk plaques [97]. A study employed a discriminant analysis ML model on spectral and attenuation data, based on acquired OCT spectra to quantify key chemicals, such as lipid, collagen, and calcium, in phantoms and swine ex vivo tissue [98]. This advancement has significant implications for personalized medicine, as it enables precise depth localization of lipids and necrotic cores in coronary plaques, improving the interpretation of IV-OCT data and facilitating tailored treatment approaches. In the field of dermatology, a U-Net model was applied to OCT results from wounds to quantify wound morphology during the healing process [69]. This approach automatically detects wound morphology and quantifies volumetric transitions throughout treatment, promising a non-invasive and real-time method for wound monitoring (Figure 8). Figure 8: Monitoring results of wound healing. After (**a**) 2 and (**b**) 7 days, images acquired with OCT are shown with their segmented (**c,d**) regions in the corresponding OCT B-scan with ML model. The annotations are: blood clot (C); dermis (D); epidermis (E); early granulation tissue (EGT); late granulation tissue (LGT); neo-epidermis (NE). Scale bar = 500 μm. Figure adapted with permission from [69]. In the field of ophthalmology, ML assists in measuring and quantifying biomarkers in different parts of the eye on OCT scans. A novel deep learning method, the residual U-Net, was introduced for the automated segmentation and quantification of choroidal thickness (CT) and vasculature [99].