Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection

12noTo assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) deep learning convolutional neural network (CNN).restrictedrestrictedAzam, Muhammad Adeel; Sampieri, Claudio; Ioppi, Alessandro; Africano, Stefano; Vallin, Alberto; Mocellin, Davide; Fragale, Marco; Guastini, Luca; Moccia, Sara; Piazza, Cesare; Mattos, Leonardo S; Peretti, GiorgioAzam, Muhammad Adeel; Sampieri, Claudio; Ioppi, Alessandro; Africano, Stefano; Vallin, Alberto; Mocellin, Davide; Fragale, Marco; Guastini, Luca; Moccia, Sara; Piazza, Cesare; Mattos, Leonardo S; Peretti, Giorgi

SmartProbe: a bioimpedance sensing system for head and neck cancer tissue detection

This study presents SmartProbe, an electrical bioimpedance (EBI) sensing system based on a concentric needle electrode (CNE). The system allows the use commercial CNEs for accurate EBI measurement, and was specially developed for in-vivo real-time cancer detection. Considering the uncertainties in EBI measurements due to the CNE manufacturing tolerances, we propose a calibration method based on statistical learning. This is done by extracting the correlation between the measured impedance value |Z| and the material conductivity \u3c3 of a group of reference materials. By utilizing this correlation, the relationship of \u3c3 and |Z| can be described as a function and reconstructed using a single measurement on a reference material of known conductivity. This method simplifies the calibration process, and is verified experimentally. Its effectiveness is demonstrate by results that show less than 6% relative error. An additional experiment is conducted for evaluating the system's capability to detect cancerous tissue. Four types of ex-vivo human tissue from the head and neck region, including mucosa, muscle, cartilage and salivary gland, are characterized using SmartProbe. The measurements include both cancer and surrounding healthy tissue excised from 10 different patients operated for head and neck cancer. The measured data is then processed using dimension reduction and analyzed for tissue classification. The final results show significant differences between pathologic and healthy tissues in muscle, mucosa and cartilage specimens. These results are highly promising and indicate a great potential for SmartProbe to be used in various cancer detection tasks

Electric Bioimpedance Sensing for the Detection of Head and Neck Squamous Cell Carcinoma

The early detection of head and neck squamous cell carcinoma (HNSCC) is essential to improve patient prognosis and enable organ and function preservation treatments. The objective of this study is to assess the feasibility of using electrical bioimpedance (EBI) sensing technology to detect HNSCC tissue. A prospective study was carried out analyzing tissue from 46 patients undergoing surgery for HNSCC. The goal was the correct identification of pathologic tissue using a novel needle-based EBI sensing device and AI-based classifiers. Considering the data from the overall patient cohort, the system achieved accuracies between 0.67 and 0.93 when tested on tissues from the mucosa, skin, muscle, lymph node, and cartilage. Furthermore, when considering a patient-specific setting, the accuracy range increased to values between 0.82 and 0.95. This indicates that more reliable results may be achieved when considering a tissue-specific and patient-specific tissue assessment approach. Overall, this study shows that EBI sensing may be a reliable technology to distinguish pathologic from healthy tissue in the head and neck region. This observation supports the continuation of this research on the clinical use of EBI-based devices for early detection and margin assessment of HNSCC