## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Assistance in Diagnosis
#### 4.1.1 Cardiovascular Diseases (CVDs)  
According to the World Health Organization (WHO), CVDs, a group of disorders related to the heart and its blood vessels, are responsible for an estimated 17.9 million deaths annually [85]. Atherosclerosis, characterized by the accumulation of substances such as lipids within the artery walls, contributes to a majority of CVDs [86]. In developed stages, atherosclerosis plaque leads to myocardial infarction, commonly known as a heart attack, by obstructing blood flow in the coronary arteries. Studies emphasize the critical role of plaque structure and composition, particularly its fibrous cap thickness as a determinant of high-risk plaques [87]. For example, thin cap fibroatheroma (TCFA), a primary type of high-risk plaque, typically exhibits a fibrous cap thickness of less than 65 um [87]. These structural and molecular plaque instability hallmarks, however, are complex and frequently not detectable from angiographic images. IV-OCT serves as a diagnostic SWIR technique for various subclinical vascular diseases (SVDs) by enabling visualization of abnormalities within cardiovascular tissues during cardiac catheterization. IV-OCT excel at detecting structural abnormalities such as atherosclerotic plaque by providing micron-resolution cross-sectional images of the arterial walls [31]. However, classification of the degree of vulnerability of plaque to rupture from IV-OCT images is frequently inaccurate due to the multifactorial nature of light-matter interactions. As such, ML models, particularly DNN, have been widely employed to automatically characterize and classify both cardiac tissue [59, 60] and plaque from IV-OCT results [56]. These studies leverage deep learning models to robustly identify various intracoronary pathological formations from OCT images. They showcase high accuracy, sensitivity, and specificity of the deep learning models, with values as high as 0.99 \(\pm\) 0.01. These advancements hold substantial promise for improving the efficiency and accuracy of CVD diagnoses, contributing to enhanced patient care and outcomes in the field of pediatric cardiology. Furthermore, supervised DNN models were used in various studies to detect, classify, and segment TCFA from IV-OCT results (Figure 6) [57, 58]. As an illustration, the automatic detection of TCFA from each B-scan through an IV-OCT pullback is rendered in a 3D map, with the cap thickness information depicted in Figure 6. In cases where supervised training with extensive labeled datasets is unfeasible, weak supervised learning methods have been proposed for TCFA detection [88]. These studies achieved high sensitivities and specificities for plaque classification, and demonstrated excellent reproducibility and efficient clinical analyses. These advancements have the potential to enhance patient care and streamline clinical workflows. In the treatment stage, a recent study assessed the effectiveness of statin-based treatments on TCFA [89]. Using ML models, the study analyzed IV-OCT results from 69 patients over 8-10 weeks to predict changes in cap thickness. When dealing with limited datasets for ML unit training, alternative ML models can be utilized. For instance, a decision-tree-based model was trained using voxels from 300 images to classify plaques [61]. Identification of stents in IV-OCT scans aids cardiologists in assessing stent deployment and tissue coverage post-implantation. Diverse ML models, including DNN [66], Bayesian networks [65], decision trees [64], and SVM [63], have been employed to detect stents in IV-OCT results. A software tool called OCTOPUS V.1, incorporating various ML models, has been developed for offline analysis of both plaques and stents [62]. Furthermore, SWIR techniques have proven valuable for analyzing Heart Failure (HF), another prevalent form of CVD, affecting over 64 million people worldwide [76]. HF is characterized by the heart's inability to efficiently fill with or pump blood due to structural or functional abnormalities.