## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Assistance in Diagnosis
#### 4.1.2 Cancer Diagnosis and Surgical Interventions  
Cancer stands as the second leading cause of death globally, accounting for nearly 10 million deaths in 2020 [90]. Surgical procedures have been employed in 45% of cases for cancer treatment [72]. Precise intraoperative tumor delineation plays a pivotal role in enhancing the effectiveness of surgical interventions. Extension of fluorescent surgery in SWIR holds promising potential to aid clinicians in achieving more precise intraoperative tumor delineation. Within neurological surgery, specifically addressing gliomas--a prevalent malignant primary nervous system tumor--conventional practices rely on surgeons' visual assessments aided by intraoperative histopathological findings from dissected tissues [50]. However, this approach lengthens procedure times and adds complexity and uncertainty. By leveraging deep learning models to analyze SWIRF data from collected tissue, real-time and highly accurate detection of tumor regions becomes achievable. In the study, employing a DNN on SWIRF data demonstrated the capacity to detect non-trivial features in images, yielding superior performance in identifying tumor regions (93.8%) compared to neurosurgeon evaluations (82.0%, \(p<0.001\)) [50]. It is important to note that due to the multifactorial nature of SWIRF signals, relying solely on pixel intensity for tumor delineation can lead to errors. To address this challenge, a study pursued tumor delineation within small animals in vivo utilizing a multispectral SWIR approach [72]. To enhance decision accuracy, captured tumor region spectra were subjected to analysis by seven distinct ML models. Results revealed that despite subtle differences between tumor and non-tumor spectra, employing KNN models increased classification accuracy to 97.1% for tumors, 93.5% for non-tumors, and 99.2% for background regions. Another study focused on distinguishing malignant kidney tissues from normal tissues in an ex vivo setting [77]. Raman spectroscopy in the SWIR range was used to obtain spectra containing various peaks, reflecting tissue conditions (normal or malignant). To classify these spectra, a Bayesian ML model called sparse multinomial logistic regression  
Figure 6: Automated detection results for TCFA from IVOCT pullbacks with ML. (**A**) Short lesion with TCFA, (**B**) long lesion with TCFA. The color bar in panel (**A**) represent the cap thickness. These three-dimensional (3D) visualizations results come from real-time analysis of each (**C**) captured cross sections by the ML model. For better visualization, panel (**D**) represents the zoomed view of the cross section and panel (**E**) indicates the zoomed view overlaid with a fibrous cap with a green color. The white asterisk (\(*\)) is the guidewire shadow. Figures adapted with permission from [57]. (SMLR) was applied. The study showcased a classification accuracy of 92.5%, sensitivity of 95.8%, and specificity of 88.8% using this ML model. Furthermore, as a pioneering advancement in the field of intraoperative surgical procedures for glioma resection in humans, a groundbreaking method has been devised leveraging multispectral fluorescence spanning the NIR and SWIR spectra (see Figure 7) [55]. The inherent high contrast images provided by SWIR technology empower surgeons to identify capillaries with unprecedented precision, detecting vessels as small as 182 \(\upmu\)m, while enhancing the certainty of isolating tumor-feeding arteries. In order to process these images in real time, a DNN equipped with a U-Net structure was harnessed for the purpose of segmenting the intricate blood vasculature. This innovative approach not only revolutionizes intraoperative procedures but also underscores the potential of advanced imaging technologies to redefine the landscape of surgical interventions. In surgical procedures, the accurate segmentation of vessels, overlapping structures, and junctions within SWIRF images emerges as a critical necessity.