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Author: search.filters.author.Chueprasert, TachasitHas files: No

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    An artificial intelligence model for the diagnosis of otitis media with effusion in children
    (2026-01-01)
    Ungkanont, Kitirat
    ;
    Udomchaiporn, Akadej
    ;
    Sriphoonga, Nopavit
    ;
    Wannarong, Thanakrit
    ;
    Rugsujrit, Thaweewat
    Background: The diagnosis of otitis media with effusion (OME) requires substantial training and experience in otoscopic examination of children. Objective: This study developed an artificial intelligence (AI) model to predict OME diagnosis in children. Methods: The source data were images of pediatric patients’ tympanic membranes obtained by otoendoscopy. A convolutional neural network was used in machine learning. The diagnostic features of the tympanic membrane, as labelled by the experts, and the surgical findings served as the ground truth. InceptionV4 built the final model. The model was trained using the Adaptive Moment Estimation optimizer with an initial learning rate of 0.0001 and a total duration of 100 epochs. The batch size was 32. The Categorical Cross-Entropy loss function was employed for the internal validation. The outcome was to distinguish between OME and normal tympanic membrane. A confusion matrix was used to assess the model’s performance. The model was tested for agreement with otolaryngologists and implemented as a web application. Results: The initial sample size was 320 pictures. For OME, the model achieved an accuracy of 94.7% (95% CI 0.88, 1). The F1 score was 96% (95% CI 0.89, 1), and the area under the receiver operating characteristic curve was 0.98 (95% CI 0.93, 1). The kappa agreement between AI and experienced otolaryngologists was 0.627 (p < 0.001). Conclusion: An AI diagnostic model for otitis media with effusion had good accuracy and moderate agreement with otolaryngologists. The model should be helpful for preliminary diagnosis, telemedicine, or educational purposes.
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    Comparative Analysis of Deep Learning Models for Building Extraction from High-resolution Satellite Imagery
    (2025-01-01)
    Chueprasert, Tachasit
    ;
    Udomchaiporn, Akadej
    ;
    Intagosum, Sarun
    In this research, an approach to extract buildings from Google's satellite imagery was proposed. The performances of various deep learning models (U-Net, RIU-Net, U-Net++, Res-U-Net, and DeepLabV3+) on pre-processed datasets were compared. The models were trained using the similarity metrics of Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). The best-performing models among the segmentation techniques were Res-U-Net and DeepLabV3+. Res-U-Net, an enhanced version of the traditional U-Net model that incorporates residual connections for improved feature propagation, achieved an F1 score of 85.43% when using the RGB dataset. Similarly, DeepLabV3+ also achieved high performance on the Enhanced RGB dataset, obtaining an F1 score of 85.18% after applying pre-processing techniques. This research highlights the significance of color as a dominant feature for accurate building extraction from satellite images. The findings contribute to improved methodologies for building identification, benefiting urban planning, and disaster management applications.
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    Death escape: A case study of merging ubiquitous activities into a hardcore computer game
    (2018-05-15)
    Yasothorn, Prawit
    ;
    Chueprasert, Tachasit
    ;
    Towongpaichayont, Witchaya
    Ubiquitous games can be designed in several settings. This paper presents a case study of designing and developing Death Escape, a role-playing survival ubiquitous game, which is intended to transform user's daily-life activities to in-game player's stats in the con-cept of 'avatar grows as the user grows'. This game is expected to blend the game mechanics with user's behaviours seamlessly. The game collects data from built-in inertia sensors in mobile phones (accelerometer and gyroscope) and GPS, utilises human medical data to transform the collected data into in-game meanings realis-tically, and presents those in-game values to motivate the user to maintain healthy behaviours. This paper describes those methods of data collections and transformations as well as additional findings during the process of design and development. This can set an example for those who are developing ubiquitous games which are blended with the user's lifestyle.