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Author: search.filters.author.Rithcharung, Preeyarat

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    Development of a Telehealth-Enabled Portable Optical Endomicroscopy System with Targeted Peptides: A Preclinical Feasibility Study for Cervical Cancer Detection
    (2026-04-01)
    Thaijiam, Chanchai
    ;
    Navaitthiporn, Nitipon
    ;
    Rithcharung, Preeyarat
    ;
    Piyawattanametha, Nicholas
    ;
    Komai, Shoji
    Background/Objectives: We developed a telehealth-enabled fiber-bundle endomicroscopy platform and evaluated its preclinical feasibility for targeted fluorescence imaging in cervical cancer models. Methods: The platform integrates a portable fiber-bundle endomicroscopy (FBE) system, fluorescein isothiocyanate (FITC)-labeled candidate peptides, and a secure web-based telehealth platform for remote consultation. The FBE probe achieved a field of view of 1,700 µm and a lateral resolution of 4 µm, enabling cellular-level fluorescence imaging in a compact, portable format. Four FITC-labeled peptides (SHS1*, SHS2*, FPP*, and CRL*) were evaluated in A549, SiHa, and CaSki cell lines. Ex vivo testing was performed on commercial cervical tissue-array samples. The telehealth platform was assessed for secure medical-image/video transmission and end-to-end latency in a simulated remote-consultation setting. Results: Among the tested probes, FPP*-FITC and CRL*-FITC showed higher fluorescence-positive fractions in the p16-overexpressing cervical cancer cell lines than in the A549 comparator line, with the strongest signals observed in CaSki cells. In ex vivo testing, CRL*-FITC generated higher fluorescence intensity in malignant cervical tissue-array samples than in non-malignant comparator tissues, with a reported 4.6- to 7.4-fold difference in mean signal intensity (p < 0.001). The telehealth platform supported the secure transmission of medical images and video and demonstrated an end-to-end latency of <500 ms in a simulated remote consultation setting. Conclusions: These results support the technical and preclinical feasibility of integrating targeted fluorescence imaging, portable fiber-bundle endomicroscopy, and telehealth into a single platform. This study should therefore be interpreted as a preclinical feasibility study evaluating optical, molecular, and telehealth integration, rather than as a clinically validated cervical cancer screening test.
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    Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images
    (2026-01-01)
    Visitsattapongse, Sarinporn
    ;
    Rithcharung, Preeyarat
    ;
    Santiprabhob, Jeerunda
    ;
    Lertbannaphong, Ornsuda
    ;
    Sermsripong, Wasawat
    Background: Diabetes mellitus (DM) is a chronic metabolic disease characterized by dysregulated blood glucose. Prolonged DM can lead to diabetic retinopathy (DR), in which retinal capillaries are damaged by sustained hyperglycemia. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality for visualizing retinal microvasculature and can detect early changes in both DM patients with and without DR. However, it requires expert evaluation, making early detection costly and time-consuming. This study aimed to develop a high-performance deep learning framework that can classify OCTA images into three groups of DM, such as normal, good glycemic control, and poor glycemic control. Methods: OCTA datasets of horizontal B-scans and en face scans from 300 participants aged 8–18 years were analyzed, including normal controls, DM patients with good glycemic control, and DM patients with poor control (HbA1c ≥8%). For each participant, a 3 mm × 3 mm foveal-centered en face image of the deep capillary plexus (DCP) and a horizontal B-scan through the foveal center of the right eye were selected. Several convolutional and transformer-based models were evaluated, with ConvNeXt (a ConvNet for the 2020s) chosen as the baseline for its superior performance. To enhance generalization and convergence, progressive resizing and the Lookahead optimization strategy were applied, while class-wise augmentation was used to balance the training set without altering the test distribution. Results: The baseline ConvNeXt achieved F1 scores of 0.7877 (B-scans) and 0.7424 (en face). After doing enhancement using progressive resizing and Lookahead optimization, performance improved to 0.8319 and 0.8567 (Wilcoxon signed-rank tests, P<0.05). Conclusions: Our proposed method for DM classification from OCTA images provided promising results while ensuring resource efficiency and rapid evaluation. Clinically, accurate classification of DM status is valuable for assessing the risk of DR progression. Thus, it can be served as an assistive tool for clinical decision support in DR management.
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    Auto focusing ophthalmoscope for smartphone
    (2020-10-29)
    Navaitthiporn, Nitipon
    ;
    Rithcharung, Preeyarat
    ;
    Wongpa, Chuttima
    ;
    Treebupachatsakul, Treesukon
    ;
    Pechprasarn, Suejit
    Several retinal pathologies can cause severe damages and may lead to permanent vision loss. Early diagnosis is highly recommended to take a precautionary measure to reduce the risk of ocular diseases. Therefore, an eye test by an ophthalmologist plays a crucial role. This does, however, require paying visit to a hospital, which becomes a burden for elders and patients with physical disabilities. Consequently, these patients usually get a late diagnosis and treatment, which the symptoms may have progressed and developed. Here, we develop an optical toolkit with engineered software for smartphones enabling the smartphone to take images of human retina as a direct ophthalmoscope. Thanks to the modern smartphone specifications, which usually come with high resolution cameras and sufficient computing power for the software. The optical toolkit illuminates an eye with an appropriate wavelength and light intensity and it has been provisionally tested for health and safety based on the ISO-10942 and IEC 62471 standards. An image processing algorithm is embedded in the software providing an auto focusing capability.
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    Intelligent glove for sign language communication
    (2019-11-01)
    Navaitthiporn, Nitipon
    ;
    Rithcharung, Preeyarat
    ;
    Hattapath, Phitnaree
    ;
    Pintavirooj, C.
    Communication is very important in daily life while many people in Thailand cannot communicate by talking. On the other hand, they can communicate only sign language. The sign language used in a specific group of people, but other people cannot understand. Therefore, we decided to do a project about glove for sign language communication. This research created a hand glove for deaf and mute people to provide better communication between disabling and normal people. We applied computer programming by used the flex sensor and GY-521 Module. The flex sensor has measured the bent of fingers and generated an analog output. GY-521 Module is use for measured the direction and movement of the hand. After got data from both sensors, it will send to Arduino IDE converted all the data to alphabet and text before converted to speech. Then we tested the accuracy of the glove, it has accuracy about 70-100% which depends on another factor. From this process, we got a hand glove for sign language communication. This glove will help deaf and mute people to communicate more efficiently.