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Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning
(2026-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Soiphet, Naruchit
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Tongloy, Teerawat
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Chuwongin, Santhad
Background: Zoonotic diseases pose a major threat to both human and animal health, contributing significantly to global morbidity and mortality. Accurate diagnosis is crucial for effective control and treatment, with microscopic examination serving as the gold standard, supplemented by highly sensitive molecular biology techniques. However, these confirmatory methods require skilled personnel and are subject to inter- and intra-rater variability. An innovative solution lies in artificial intelligence (AI)-powered automated tools, which offer a promising alternative. This study aimed to develop a self-supervised learning (SSL) approach using the Distillation with No Labels (DiNOv2) algorithm to extract features of protozoa from Giemsa-stained blood samples. Methods: The development of self-supervised learning algorithms, including DiNOv2, was based on archived samples of clinically significant and significant veterinary microorganisms. These models were evaluated in comparison to a baseline Vision Transformer (ViT). Results: Among the tested SSL models, the DiNOv2-Small version achieved exceptional performance, surpassing 99% accuracy and specificity while maintaining the lowest misclassification rate (0.263). It also demonstrated a high area under the curve (AUC) value of 0.990, underscoring its robust classification capability. Remarkably, even when trained on only 20% of the dataset, the SSL models retained performance levels comparable to baseline models trained on the full dataset. However, further reducing the sample size below 20% led to notable declines in evaluation metrics: accuracy dropped by 5–7%, recall decreased from 37.9% to 35.1%, precision fell from 35% to 25.2%, and the F1 score declined from 31.1% to 21.5%. Additionally, the AUC decreased by 4–11%, while the misclassification rate increased, indicating reduced robustness. A key limitation of this study was the highly imbalanced fine-tuned dataset between classes. Nevertheless, the inconsistent performance observed may be mitigated by employing the larger DiNOv2 model, which improves the F1 score and enhances the model’s ability to handle imbalanced data while reducing reliance on labeled data. Conclusions: The proposed method can assist laboratory technicians, particularly in resource-limited healthcare settings. Furthermore, the findings support the potential deployment of this AI-based tool for automated screening in both medical and veterinary applications.
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Benchmarking Next-Generation Frontier Models for Document-Level Arabic-Thai Medical Translation: A Reliability Study of LLM-as-a-Judge
(2026-01-01)
Lertsuksakda, Rathawut
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Netisopakul, Ponrudee
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Boonsang, Siridech
This research targets Arabic-to-Thai medical translation, a low-resource and linguistically distant pair critical for public health. We utilize the OpenWHO dataset, a resource comprising 293 documents. This dataset is shielded from webcrawling to minimize training data contamination and ensure rigorous zero-shot evaluation. We conduct a comparative analysis between 2026-era frontier models including GPT-5.2, Gemini 3 Pro, and Claude Opus 4.5 and industry-standard NMT represented by Google Translate. Using an LLM-as-a-Judge framework with an AI jury of efficient reasoning models such as GPT-5.1, Gemini 3 Flash, and Claude Sonnet 4.5, we performed 3,516 evaluations of fidelity, fluency, and cultural appropriateness. Results demonstrate that frontier models outperform traditional NMT across all dimensions. While high exact agreement suggests LLM-as-a-Judge frameworks are promising for scalable evaluation, human validation reveals persistent opportunities for improving AI-human alignment, necessitating targeted oversight for safety-critical applications.
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Performance validation of deep-learning-based approach in stool examination
(2025-12-01)
Corpuz, Kristal Dale Felimon
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Kusolsuk, Teera
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Wongphan, Benjamaporn
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Chonsawat, Putza
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Naing, Kaung Myat
Background: Human intestinal parasitic infections (IPI) pose a significant global health issue caused by parasitic helminths and protozoa, affecting around 3.5 billion people worldwide, with more than 200,000 deaths annually. Despite advancements in molecular methods with higher sensitivity and specificity, the Kato-Katz or formalin-ethyl acetate centrifugation technique (FECT) remains the gold standard and a routine diagnostic procedure suitable for its simplicity and cost-effectiveness. However, these techniques have limitations that must be addressed. Thus, this study evaluated the performance of a deep-learning-based approach for intestinal parasite identification and compared it with that of human experts. Methods: Human experts performed FECT and Merthiolate-iodine-formalin (MIF) techniques to serve as ground truth and reference for parasite species. Subsequently, a modified direct smear was conducted to gather images for the training (80%) and testing (20%) datasets. State-of-the-art models, including YOLOv4-tiny, YOLOv7-tiny, YOLOv8-m, ResNet-50, and DINOv2 (base, small, and large), were employed and were operated using in-house CIRA CORE platform. Overall performance was evaluated using confusion matrices, the metrics of which were calculated on the basis of the one-versus-rest and micro-averaging approaches. Moreover, the receiver operating characteristic (ROC) and precision-recall (PR) curves were determined for visual comparison. Lastly, Cohen’s Kappa and Bland–Altman analyses were used to statistically measure the significant differences and visualize the association levels between the human experts and the deep learning models’ classification performance in intestinal parasite identification. Results: Findings demonstrated the potential of a deep-learning-based approach, particularly of models DINOv2-large (accuracy: 98.93%; precision: 84.52%; sensitivity: 78.00%; specificity: 99.57%; F1 score: 81.13%; AUROC: 0.97) and YOLOv8-m (accuracy: 97.59%; precision: 62.02%; sensitivity: 46.78%; specificity: 99.13%; F1 score: 53.33%; AUROC: 0.755; AUPR: 0.556) for their high metric values in intestinal parasite identification. Class-wise prediction showed high precision, sensitivity, and F1 scores for helminthic eggs and larvae due to more distinct morphology. Moreover, all models obtained a > 0.90 k score, which indicates a strong level of agreement compared with the medical technologists. The Bland–Altman analysis also presented the best agreement between FECT performed by medical technologist A and YOLOv4-tiny, while the MIF technique performed by medical technologist B and DINOv2-small demonstrated the best bias-free agreement, with mean differences of 0.0199 and −0.0080, and standard deviation differences of 0.6012 and 0.5588, respectively. Conclusions: The results highlight the potential of integrating a deep-learning-based approach into parasite identification. The models showcased superiority in automated detection, suggesting a significant leap toward improving diagnostic procedures for IPI. This hybridization could enhance early detection and diagnosis, facilitating timely and targeted interventions to reduce the burden of IPI through more effective management and prevention strategies.
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A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases
(2025-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Arnuphaprasert, Apinya
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Jomtarak, Rangsan
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Naing, Kaung Myat
Anaplasmosis, which is caused by Anaplasma spp. and transmitted by tick bites, is one of the most serious livestock animal diseases worldwide, causing significant economic losses as well as public health issues. Anaplasma marginale, a gram-negative intracellular obligate bacterium, can cause disease in cattle and other ruminants. Because of the insufficient quality of the slides, a microscopic diagnostic procedure is time-consuming and challenging to diagnose. Intra- and inter-rater variation is frequently imposed on by technicians who are underqualified and unexperienced. Alternatively, algorithms could support local employees in tracking disease transmission and quick action, especially in Thailand where this cattle disease is common. As a result, the study intends to create an automated tool based on a deep neural network linked with an image-retrieval procedure for recognizing infections in microscopic pictures. The Resnext-50 model, which serves as the embedding space’s backbone and is optimized by Triplet-Margin loss, outperforms, with averaged accuracy and specificity ratings of 91.30 percent and 92.83 percent, respectively. The model’s performance was also improved by a fine-tuned procedure between k-nearest neighbor and its normalized distance of each data point, including precision of 0.833 ± 0.134, specificity of 0.930 ± 0.054, recall of 0.838 ± 0.118, and accuracy of 0.915 ± 0.025, respectively. Five-fold cross-validation confirms that the trained model using the optimal k-nearest neighbor (kNN) for the image-based retrieval system, involving 12 images, prevents overfitting via dataset variations indicating areas under the receiver operating curve rankings ranging from 0.917 to 0.922. The image retrieval technique demonstrated in this research is a prototype for a variety of applications. The findings may aid in the early diagnosis of anaplasmosis infections in remote areas without access to veterinary care or costly molecular diagnostic tools.
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Modification of silk fibroin film structure: From silkworm diet to material properties
(2025-06-01)
Kaewpirom, Supranee
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Teangtam, Watcharin
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Monthatong, Monthira
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Boonsang, Siridech
This study investigates the structural modification of silk fibroin films through dietary supplementation of Bombyx mori silkworms with probiotics (lived L. pentosus SF66) and paraprobiotics (heat-killed L. pentosus SF66). Physical appearance, tensile properties, and morphology of reeled silk fibers were characterized. Fibers were purified and dialyzed to obtain aqueous silk fibroin solutions, which were cast into regenerated films (CON-F, PRO[sbnd]F, and PARA-F). Dietary supplementation influenced the film-forming properties of silk fibroin. Fourier-transform infrared spectroscopy and wide-angle X-ray diffraction analyses revealed that crystallinity decreased in the order CON-F > PARA-F > PRO-F, corresponding to reduced β-sheet content and increased random coil conformations. Both probiotic and paraprobiotic supplementation demonstrated mechanisms for modulating silk fibroin conformational transitions and structural organization. Real-time polymerase chain reaction analysis of key silk fibroin genes (fibroin heavy chain, fibroin light chain (fib-l), and 25-kD polypeptide protein) demonstrated elevated expression in the paraprobiotic-supplemented group, with fib-l showing the most pronounced upregulation. A mechanistic model explaining how these nutritional interventions affect silk film formation is proposed. These findings provide insights for developing targeted nutritional strategies to modify the secondary protein structure of regenerated silk films.
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Deep Learning Based Visual Servo for Autonomous Aircraft Refueling
(2025-03-01)
Phatthamolrat, Natthaphop
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Tongloy, Teerawat
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Boonsang, Siridech
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Chuwongin, Santhad
This study develops and evaluates a deep learning based visual servoing (DLBVS) control system for guiding industrial robots during aircraft refueling, aiming to enhance operational efficiency and precision. The system employs a monocular camera mounted on the robot's end effector to capture images of target objects—the refueling nozzle and bottom loading adapter—eliminating the need for prior calibration and simplifying real-world implementation. Using deep learning, the system identifies feature points on these objects to estimate their pose estimation, providing essential data for precise manipulation. The proposed method integrates two-stage neural networks with the Efficient Perspective-n-Point (EPnP) principle to determine the orientation and rotation angles, while an approximation principle based on feature point errors calculates linear positions. The DLBVS system effectively commands the robot arm to approach and interact with the targets, demonstrating reliable performance even under positional deviations. Quantitative results show translational errors below 0.5 mm and rotational errors under 1.5° for both the nozzle and adapter, showcasing the system's capability for intricate refueling operations. This work contributes a practical, calibration-free solution for enhancing automation in aerospace applications. The videos and data sets from the research are publicly accessible at https://tinyurl.com/CiRAxDLBVS.
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Tailoring silk fibroin hydrophilicity and physicochemical properties using sugar alcohols for medical device coatings
(2024-12-01)
Kaewpirom, Supranee
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Piboonnithikasem, Sarayoot
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Sroisroemsap, Pongsathorn
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Uttoom, Sittichai
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Boonsang, Siridech
This study explores the modification of silk fibroin films for hydrophilic coating applications using various sugar alcohols. Films, prepared via solvent casting, incorporated glycerol, sorbitol, and maltitol, revealing distinctive transparency and UV absorption characteristics based on sugar alcohol chemical structures. X-ray diffraction confirmed a silk I to silk II transition influenced by sugar alcohols. Glycerol proved most effective in enhancing the β-sheet structure. The study also elucidated a conformational shift towards a β-sheet structure induced by sugar alcohols. Silk fibroin–sugar alcohol blind docking and sugar alcohol-sugar alcohol blind docking investigations were conducted utilizing the HDOCK Server. The computer simulation unveiled the significance of size and hydrogen bonding characteristics inherent in sugar alcohols, emphasizing their pivotal role in influencing interactions within silk fibroin matrices. Hydrophilicity of ozonized silicone surfaces improved through successful coating with silk fibroin films, particularly glycerol-containing ones, resulting in reduced contact angles. Strong adhesion between silk fibroin films and ozonized silicone surfaces was evident, indicating robust hydrogen bonding interactions. This comprehensive research provides crucial insights into sugar alcohols’ potential to modify silk fibroin film crystalline structures, offering valuable guidance for optimizing their design and functionality, especially in silicone coating applications.
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Enhancing mosquito classification through self-supervised learning
(2024-12-01)
Charoenpanyakul, Ratana
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Kittichai, Veerayuth
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Eiamsamang, Songpol
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Sriwichai, Patchara
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Pinetsuksai, Natchapon
Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model’s overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies.
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Enhance fashion classification of mosquito vector species via self-supervised vision transformer
(2024-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Chaiphongpachara, Tanawat
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Laojun, Sedthapong
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Saiwichai, Tawee
Vector-borne diseases pose a major worldwide health concern, impacting more than 1 billion people globally. Among various blood-feeding arthropods, mosquitoes stand out as the primary carriers of diseases significant in both medical and veterinary fields. Hence, comprehending their distinct role fulfilled by different mosquito types is crucial for efficiently addressing and enhancing control measures against mosquito-transmitted diseases. The conventional method for identifying mosquito species is laborious and requires significant effort to learn. Classification is subsequently carried out by skilled laboratory personnel, rendering the process inherently time-intensive and restricting the task to entomology specialists. Therefore, integrating artificial intelligence with standard taxonomy, such as molecular techniques, is essential for accurate mosquito species identification. Advancement in novel tools with artificial intelligence has challenged the task of developing an automated system for sample collection and identification. This study aims to introduce a self-supervised Vision Transformer supporting an automatic model for classifying mosquitoes found across various regions of Thailand. The objective is to utilize self-distillation with unlabeled data (DINOv2) to develop models on a mobile phone-captured dataset containing 16 species of female mosquitoes, including those known for transmitting malaria and dengue. The DINOv2 model surpassed the ViT baseline model in precision and recall for all mosquito species. When compared on a species-specific level, utilizing the DINOv2 model resulted in reductions in false negatives and false positives, along with enhancements in precision and recall values, in contrast to the baseline model, across all mosquito species. Notably, at least 10 classes exhibited outstanding performance, achieving above precision and recall rates exceeding 90%. Remarkably, when applying cropping techniques to the dataset instead of utilizing the original photographs, there was a significant improvement in performance across all DINOv2 models studied. This is demonstrated by an increase in recall to 87.86%, precision to 91.71%, F1 score to 88.71%, and accuracy to 98.45%, respectively. Malaria mosquito species can be easily distinguished from another genus like Aedes, Mansonia, Armigeres, and Culex, respectively. While classifying malaria vector species presented challenges for the DINOv2 model, utilizing the cropped images enhanced precision by up to 96% for identifying one of the top three malaria vectors in Thailand, Anopheles minimus. A proficiently trained DINOv2 model, coupled with effective data management, can contribute to the development of a mobile phone application. Furthermore, this method shows promise in supporting field professionals who are not entomology experts in effectively addressing pathogens responsible for diseases transmitted by female mosquitoes.
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Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning
(2024-11-01)
Suksangvoravong, Hathaiphat
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Choisunirachon, Nan
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Tongloy, Teerawat
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Chuwongin, Santhad
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Boonsang, Siridech
Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians. In this study, an artificial intelligence model was developed to screen canine tracheal collapse using archived lateral cervicothoracic radiographs. This model can differentiate between a normal and collapsed trachea, ranging from early to severe degrees. The you-only-look-once (YOLO) models, including YOLO v3, YOLO v4, and YOLO v4 tiny, were used to train and test data sets under the in-house XXX platform. The results showed that the YOLO v4 tiny-416 model had satisfactory performance in screening among the normal trachea, grade 1–2 tracheal collapse, and grade 3–4 tracheal collapse with 98.30% sensitivity, 99.20% specificity, and 98.90% accuracy. The area under the curve of the precision–recall curve was >0.8, which demonstrated high diagnostic accuracy. The intraobserver agreement between deep learning and radiologists was κ = 0.975 (P <.001), with all observers having excellent agreement (κ = 1.00, P <.001). The intraclass correlation coefficient between observers was >0.90, which represented excellent consistency. Therefore, the deep learning model can be a useful and reliable method for effective screening and classification of the degree of tracheal collapse based on routine lateral cervicothoracic radiographs.

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