## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Assistance in Diagnosis
#### 4.1.2 Cancer Diagnosis and Surgical Interventions
A previous study has demonstrated the application of DNN for vasculature segmentation purposes, in preclinical phases as a proof of concept [52]. The Iter-Net model, originally devised for retinal segmentation, accurately delineated murine vasculature captured in vivo using SWIRF  
Figure 7: Showcase of blocking feeding arteries and tumor resection of a patient with GBM using the image-guided method. **(a)** MRI scans before surgery indicated a lesion in the right parietal (red circle). **(b)** Scan results with visible light after dural opening. **(c)** Scan of tumor with SWIRF method. NIR-IIb image of the **(d)** feeding arteries and **(e)** veins. **(f)** Integrated image of these separate scans. White arrows 1–6 correspond to the location of the tumor-feeding arteries. **(g)** Yellow lines show the feeding arteries blocked as a result of tumor resection during surgery. Panel 1–6 correspond to the vessels labeled as 1–6 in **(f)**. **(h)** MRI scan shows the tumor was resected to the maximum extent after this image-guided surgery. Figure adapted with permission from [55]. imaging. Remarkably, the DNN not only achieved effective segmentation, but also collected supplementary vascular insights encompassing morphological details, discernment of vessel types (veins or arteries), and characterization of hemodynamic attributes. These accomplishments hold the potential to extend into the SWIRF-guided surgery, offering valuable assistance to surgeons during operative interventions. An innovative method was reported for detecting tumoral epithelial lesions through the synergy of hyperspectral imaging and deep learning. Detecting these lesions early is crucial for effective cancer diagnosis and treatment planning. DNN RetinaNet was then utilized to automatically identify and classify these lesions. The findings highlight the effectiveness of this approach, offering a promising non-invasive solution for early-stage cancer detection, with potential implications for enhancing patient care and treatment strategies [91]. OCT and its extended modalities have gained prominence in cancer-related applications, due to the fine resolution, high acquisition rate, and millimeter-scale imaging depth, especially in intraoperative tumor margin evaluation. Moreover, compatibility of OCT with fiber optic technology enables miniaturization of its imaging head into diverse portable formats, such as handheld, needle probes, and single fibers, which enables precise assessment of tumor margins during breast-conserving surgeries [28]. By harnessing DNN, clinical OCT datasets sourced from patients with breast tumors have achieved remarkable outcomes, indicating a 98.9% accuracy in the classification of 44 normal and 44 malignant cases [92]. Additionally, the fusion of ultrahigh-resolution OCT systems operating in NIR and SWIR ranges, coupled with the assistance of a Relevance Vector Machine ML model, demonstrated an automatic detection capability for invasive ductal carcinoma within ex vivo human breast tissue, achieving an overall accuracy of 84% [93]. Further demonstrating the efficacy of OCT, a study employing an SVM model to classify OCT images from cancerous tissues yielded exceptional metrics with sensitivity, specificity, and accuracy values of 91.56%, 93.86%, and 92.71%, respectively, [94]. In microscopy, the integration of OCT and Raman spectroscopy yielded a comprehensive set of morphological, intensity, and spectroscopic features for ML models aimed at classifying cancer cells in an in vitro setting. By subjecting the images to analysis using three distinct ML models, namely linear discriminant analysis, KNN, and decision tree, the study achieved an impressive classification accuracy of 85% for distinguishing among five different types of skin cells [95]. A recent advancement involves the application of polarization sensitive (PS)-OCT, as a modality of OCT to provide higher contrast for differentiation and classification of malignant tumors, fibro-adipose tissues, and stroma within human excised breast tissues [96].