Piyawattanametha, Wibool

Preferred name

Main Affiliation

School of Engineering

wibool.pi@kmitl.ac.th

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3CA-FO: Budget stereoscopic 3D imaging colposcope
(2023-01-01)
Amnuayphol, Nontiwat
;
Piyawattanametha, Wibool
;
Piyawattanametha, Nicholas
This research aims to develop an advanced medical device designed to enhance the diagnostic quality of conventional colposcopy. The device utilizes cutting-edge technologies, including 3D image synthesis via stereoscopic imaging and polarized glasses as the primary focus of the study is to improve cervical cancer. The research scope encompasses enhancing spatial information, The research scope involves enhancing spatial information while allowing the doctor to maintain the advantage of near vision and enabling multi-angle imaging. The hardware of the colposcope is based on the design from Duke University's 2018 research. Our 3D Cervical Assessment - Fine-tuned Optics colposcope (3CA-FO) is capable of providing real-time 3D imaging with precise calibration, achieved through the utilization of the Embedded Mono Calibration for Heterogeneous Lenses technique. This ensures an instantaneous and high-quality 3D output response.
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Development of a Low-Cost Raman Spectrometer for Non-Invasive Molecular Mapping
(2024-01-01)
Tangkiatphaibun, Parawee
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Suttikittipong, Pasin
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Udomtanasub, Pholchanok
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Piyawattanametha, Nicholas
;
Piyawattanametha, Wibool
Non-communicable diseases like diabetes continue to be major global health challenges, traditionally managed through invasive glucose monitoring methods. To address this, our research focuses on developing a DIY Raman spectrometer enhanced with a fiber laser for non-invasive glucose detection. Utilizing Surface-Enhanced Raman Spectroscopy (SERS) with a SERS S440 substrate at 500 ppm, we successfully achieved Raman mapping and demonstrated the capability to capture Raman shifts across various samples. The mapping results indicate the system's effectiveness in visualizing molecular composition, laying a strong foundation for future integration of SERS multiplexing to further enhance sensitivity and enable simultaneous detection of multiple analytes. This approach presents a cost-effective and accessible solution, showcasing the potential of advanced Raman spectroscopy in diabetes care and non-invasive monitoring technologies. Our work represents a significant step toward improving the accessibility and accuracy of glucose monitoring for patients globally.
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Multiplexed Surface-Enhanced Raman Mapping via DIY Spectrometer for High-Specificity Molecular Detection
(2025-01-01)
Tangkiatphaibun, Parawee
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Suttikittipong, Pasin
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Udomtanasub, Pholchanok
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Piyawattanametha, Aaron
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Piyawattanametha, Nicholas
In this research, we aim to build a low-cost Do-It-Yourself(DIY) Raman spectrometer system. Due to the previous limitation, the surface-enhanced Raman spectroscopy(SERS) multiplexing for enhance the Raman fingerprint of the benchmark substrate. The previous research was done at a single point, then 3×2 multiplex mapping only. This study enables the 4×4 multiplex mapping method using SERS substrates. Expanding beyond previous work on single-point detection, this study demonstrates a 4×4 multiplex mapping method using SERS substrates (S440) nanoparticle-based substrate. This allows a deeper range of the DIY Raman to get multiple Raman fingerprints from the same substrate and leads to the future artificial intelligence(AI) identification of the substrate complexity and the quantitative analysis. The system is based on a 785nm fiber laser and a custom optics setup.
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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
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Navaitthiporn, Nitipon
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Rithcharung, Preeyarat
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Piyawattanametha, Nicholas
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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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Comparison of MEMS-based photoacoustic microscopy in biomedical imaging
(2025-01-01)
Suttikittipong, Pasin
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Tangkiatphaibun, Parawee
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Piyawattanametha, Nicholas
;
Piyawattanametha, Wibool
Photoacoustic microscopy (PAM) has emerged as a promising biomedical imaging technique, renowned for its capability to visualize the microvasculature and measure oxygen saturation levels in biological tissues, both non-invasively and in real-time. PAM combines the contrast benefits of optical imaging with the penetrating benefits of ultrasound. It offers high spatial resolution and deep tissue imaging, which is what microscopic and macroscopic imaging can’t do separately. This review presents fundamental knowledge of PAM’s theoretical model and the sensing mechanism. Using a Micro-Electro-Mechanical Systems or Microelectromechanical Systems (MEMS), various PAM optimizing techniques are covered, ranging from design, materials, and algorithms. Discussions also include opinions on future MEMS-based PAM technology development tendencies.
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Mammogram Analysis with YOLO Models on an Affordable Embedded System
(2026-01-01)
Intasam, Anongnat
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Piyawattanametha, Nicholas
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Promworn, Yuttachon
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Jiranantanakorn, Titipon
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Thawornwanchai, Soonthorn
Background/Objectives: Breast cancer persists as a leading cause of female mortality globally. Mammograms are a key screening tool for early detection, although many resource-limited hospitals lack access to skilled radiologists and advanced diagnostic tools. Deep learning-based computer-aided detection (CAD) systems can assist radiologists by automating lesion detection and classification. This study investigates the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (YOLOv5, YOLOv8, YOLOv10, YOLOv11, and Real-Time-DEtection Transformer (RT-DETR)) for detecting mammographic lesions on an affordable embedded system. Methods: We developed a custom web-based annotation tool to enhance mammogram labeling accuracy, using a dataset of 3169 patients from Thailand and expert annotations from three radiologists. Lesions were classified into six categories: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM). Results: Our results show that the YOLOv11n model is the optimal choice for the NVIDIA Jetson Nano, achieving an accuracy of 0.86 and an inference speed of 6.16 ± 0.31 frames per second. A comparative analysis with a graphics processing unit (GPU)-powered system revealed that the Jetson Nano achieves comparable detection performance at a fraction of the cost. Conclusions: The current research landscape has not yet integrated advanced YOLO versions for embedded deployment in mammography. This method could facilitate screening in clinics without high-end workstations, demonstrating the feasibility of deploying CAD systems in low-resource environments and underscoring its potential for real-world clinical applications.
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Piyawattanametha, Wibool

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