Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection

Abstract

This aim of this study was to find effective spectral bands for the early detection of oral cancer. The spectral images in different bands were acquired using a self-made portable light-emitting diode (LED)-induced autofluorescence multispectral imager equipped with 365 and 405 nm excitation LEDs, emission filters with center wavelengths of 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm, and a color image sensor. The spectral images of 218 healthy points in 62 healthy participants and 218 tumor points in 62 patients were collected in the ex vivo trials at China Medical University Hospital. These ex vivo trials were similar to in vivo because the spectral images of anatomical specimens were immediately acquired after the on-site tumor resection. The spectral images associated with red, blue, and green filters correlated with and without nine emission filters were quantized by four computing method, including summated intensity, the highest number of the intensity level, entropy, and fractional dimension. The combination of four computing methods, two excitation light sources with two intensities, and 30 spectral bands in three experiments formed 264 classifiers. The quantized data in each classifier was divided into two groups: one was the training group optimizing the threshold of the quantized data, and the other was validating group tested under this optimized threshold. The sensitivity, specificity, and accuracy of each classifier were derived from these tests. To identify the influential spectral bands based on the area under the region and the testing results, a single-layer network learning process was used. This was compared to conventional rules-based approaches to show its superior and faster performance. Consequently, four emission filters with the center wavelengths of 470, 505, 532, and 550 nm were selected by an AI-based method and verified using a rule-based approach. The sensitivities of six classifiers using these emission filters were more significant than 90%. The average sensitivity of these was about 96.15%, the average specificity was approximately 69.55%, and the average accuracy was about 82.85%.

Clinical assessment of a low-cost, hand-held, smartphone-attached intraoral imaging probe for 5-aminolevulinic acid photodynamic therapy monitoring and guidance

“…Overall the smartphone-coupled intraoral device successfully integrated features of existing auto-FL imaging devices 62 – 66 while achieving the intended suitability for remote and rural healthcare settings. (1) It is portable, lightweight, durable, sustainable, and affordable.…”

Emerging clinical applications in oncology for non‐invasive multi‐ and hyperspectral imaging of cell and tissue autofluorescence

“…Optimum discrimination (sensitivity 84.68%, specificity 76.24% and accuracy 80.66%) was achieved by illumination with the 365 nm LED and no filters. In a follow-up study [47], 365 and 405 nm excitation LEDs were applied with emission filters with centre wavelengths at 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm. Single-layer network processing was used to select six classifiers using the 470, 505, 532 and 550 nm emission filters which could predict the presence of oral cancer with a sensitivity 96.15%, specificity 69.55% and accuracy 82.85%.…”

“…Cancers that have been investigated using hyper/ multispectral imaging include colon [35][36][37][38][39], oral [40][41][42][43][44][45][46][47][48], head and neck [49][50][51][52][53], skin [54,55], breast [56,57], cervical [58], gastric [59], ocular [17,20], bladder [60], lung [61], brain [62,63], and ovarian [64] (Table 1 It is readily apparent that work has focused on accessible tumours that can be reached endoscopically (e.g., colon and oral) or external tumours (e.g., ocular and skin). This is unsurprising as this optical imaging technology is best suited for application in areas of the body which do not require surgery to access.…”

“…Cancers that have been investigated using hyper/multispectral imaging include colon [35–39], oral [40–48], head and neck [49–53], skin [54, 55], breast [56, 57], cervical [58], gastric [59], ocular [17, 20], bladder [60], lung [61], brain [62, 63], and ovarian [64] (Table 1). Research interest in this area is intensifying, with seven publications published 4 years covered by this scoping review, eight in the second, and 16 in the most recent.…”