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Visible-light optical coherence tomography-based multimodal system for quantitative fundus autofluorescence imaging
摘要： Fundus autofluorescence (FAF) imaging is commonly used in ophthalmic clinics for diagnosis and monitoring of retinal diseases. Lipofuscin in the retinal pigment epithelium (RPE), with A2E as its most abundant component and a visual cycle by-product, is the major fluorophore of FAF. Lipofuscin accumulates with age and is implicated in degenerative retinal diseases. The amount of lipofuscin in RPE can be assessed by quantitative measurement of FAF. However, the currently available FAF imaging technologies are not capable of quantifying the absolute intensity of FAF, which is essential for comparing images from different individuals, and from the same individual over time. One major technical difficulty is to compensate the signal attenuation by ocular media anterior to the RPE (pre-RPE media). FAF intensity is also influenced by fluctuations in imaging conditions such as illumination power and detector sensitivity, all of which need to be compensated. In this review, we present the concept and research progress of using visible-light optical coherence tomography-based simultaneous multimodal retinal imaging to compensate signal attenuation by pre-RPE media and the influence of parameters of the acquisition system for accurate measurement of FAF intensities.
关键词： fundus autofluorescence imaging,multimodal imaging,retinal pigment epithelium lipofuscin,Visible-light optical coherence tomography,retinal imaging,fluorescence quantification
更新于2025-09-09 09:28:46
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Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities
摘要： Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructure, implements autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform, enabling early detection of pre-cancerous and cancerous lesions in the oral cavity with the potential to reduce morbidity, mortality, and overall healthcare costs. Using a custom Android application, this device synchronizes external light-emitting diode (LED) illumination and image capture for AFI and WLI. Data is uploaded to a cloud server for diagnosis by a remote specialist through a web app, with the ability to transmit triage instructions back to the device and patient. Finally, with the on-site specialist’s diagnosis as the gold-standard, the remote specialist and a convolutional neural network (CNN) were able to classify 170 image pairs into ‘suspicious’ and ‘not suspicious’ with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81.25% to 94.94%.
关键词： convolutional neural network,screening device,oral cancer,low-resource communities,smartphone-based,autofluorescence imaging,white light imaging
更新于2025-09-09 09:28:46