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

SIGNIFICANCE: India has one of the highest rates of oral squamous cell carcinoma (OSCC) in the world, with an incidence of 15 per 100,000 and more than 70,000 deaths per year. The problem is exacerbated by a lack of medical infrastructure and routine screening, especially in rural areas. New technologies for oral cancer detection and timely treatment at the point of care are urgently needed. AIM: Our study aimed to use a hand-held smartphone-coupled intraoral imaging device, previously investigated for autofluorescence (auto-FL) diagnostics adapted here for treatment guidance and monitoring photodynamic therapy (PDT) using 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence (FL). APPROACH: A total of 12 patients with 14 buccal mucosal lesions having moderately/well-differentiated micro-invasive OSCC lesions (1.65 at the time of treatment were associated with successful outcomes. CONCLUSION: These results indicate the utility of a low-cost, handheld intraoral imaging probe for image-guided PDT and treatment monitoring while also laying the groundwork for an integrated approach, combining cancer screening and treatment with the same hardware

Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding

Point-of-care, multispectral, smartphone-based dermascopes for dermal lesion screening and erythema monitoring

Significance: The rates of melanoma and nonmelanoma skin cancer are rising across the globe. Due to a shortage of board-certified dermatologists, the burden of dermal lesion screening and erythema monitoring has fallen to primary care physicians (PCPs). An adjunctive device for lesion screening and erythema monitoring would be beneficial because PCPs are not typically extensively trained in dermatological care. Aim: We aim to examine the feasibility of using a smartphone-camera-based dermascope and a USB-camera-based dermascope utilizing polarized white-light imaging (PWLI) and polarized multispectral imaging (PMSI) to map dermal chromophores and erythema. Approach: Two dermascopes integrating LED-based PWLI and PMSI with both a smartphonebased camera and a USB-connected camera were developed to capture images of dermal lesions and erythema. Image processing algorithms were implemented to provide chromophore concentrations and redness measures. Results: PWLI images were successfully converted to an alternate colorspace for erythema measures, and the spectral bandwidth of the PMSI LED illumination was sufficient for mapping of deoxyhemoglobin, oxyhemoglobin, and melanin chromophores. Both types of dermascopes were able to achieve similar relative concentration results. Conclusion: Chromophore mapping and erythema monitoring are feasible with PWLI and PMSI using LED illumination and smartphone-based cameras. These systems can provide a simpler, more portable geometry and reduce device costs compared with interference-filter-based or spectrometer-based clinical-grade systems. Future research should include a rigorous clinical trial to collect longitudinal data and a large enough dataset to train and implement a machine learning-based image classifier.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Compliance with Specialist Referral for Increased Cancer Risk in Low-Resource Settings: In-Person vs. Telehealth Options.

Efforts are underway to improve the accuracy of non-specialist screening for oral cancer (OC) risk, yet better screening will only translate into improved outcomes if at-risk individuals comply with specialist referral. Most individuals from low-resource, minority, and underserved (LRMU) populations fail to complete a specialist referral for OC risk. The goal was to evaluate the impact of a novel approach on specialist referral compliance in individuals with a positive OC risk screening outcome. A total of 60 LRMU subjects who had screened positive for increased OC risk were recruited and given the choice of referral for an in-person (20 subjects) or a telehealth (40 subjects) specialist visit. Referral compliance was tracked weekly over 6 months. Compliance was 30% in the in-person group, and 83% in the telehealth group. Approximately 83-85% of subjects from both groups who had complied with the first specialist referral complied with a second follow-up in-person specialist visit. Overall, 72.5% of subjects who had chosen a remote first specialist visit had entered into the continuum of care by the study end, vs. 25% of individuals in the in-person specialist group. A two-step approach that uses telehealth to overcome barriers may improve specialist referral compliance in LRMU individuals with increased OC risk