Chawuthai, Rathachai

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Chawuthai, Rathachai
Alternative Name
Chawuthai, R.
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Email
rathachai.ch@kmitl.ac.th

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    Item type:Publication,
    Eye Landmarks Detection using RT-DETR with Rules
    (2024-01-01)
    Boonnithititikul, Chatree
    ;
    Jaknamon, Teetouch
    ;
    Chawuthai, Rathachai
    In order to help ophthalmologists diagnose eye problems, it is necessary to scan for eye landmarks such as the pupil, the reflection point on the retina, and the boundary of the eye. An individual's eye landmarks on their face can be obtained via some facial landmarks' detection methods, including Haar Cascade. Two problematic aspects of the current approaches, however, are that the pupil and reflection point information is not provided, and the detection is ineffective when confronted with a picture of the upper half of the face or a person wearing a mask. In this study, we intend to develop a deep learning model for eye landmark identification using the Realtime identification Transformer (RT-DETR) approach together with our rules. As a consequence, nine landmark points-two for the eye, six for the pupil, and one for the reflection, are computed with an accuracy of 0.974. Since the focus of this paper is on eye landmark recognition, the next stage will be to build an application and a machine learning model for the diagnosis of eye disorders. - Keywords Deep Learning, Detection, Eye Landmarks, Facial Landmarks, Ophthalmology, RT-DETR
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    Utilizing deep learning from mobile phone photos for early detection of horizontal strabismus: a screening approach
    (2026-12-01)
    Chawuthai, Rathachai
    ;
    Sermswan, Anawat
    ;
    Boonnithititikul, Chatree
    ;
    Hokierti, Kiatthida
    ;
    Sermsripong, Wasawat
    To develop and validate an artificial intelligence pipeline for binary screening of horizontal strabismus versus orthotropia using smartphone-acquired facial images and geometric landmark analysis. This two-stage system combines Real-Time Detection Transformer (RT-DETR) to localize nine ocular landmarks per eye across three gaze directions (left, center, right), and supervised machine learning classifiers. A feature set of five biometric ratios was derived from coordinates including the canthi, limbi, and corneal light reflexes. The model was trained on facial images from 150 participants (96 with strabismus and 54 controls). To address class imbalance and improve generalizability, Synthetic Minority Oversampling Technique (SMOTE) and 4-fold cross-validation were applied. RT-DETR achieved an intersection over union of 0.62 and a mean center-point error of 6.52 pixels in landmark localization. The Random Forest classifier achieved an accuracy of 0.95, sensitivity of 0.96, specificity of 0.94, positive predictive value of 0.97, and negative predictive value of 0.92. This study demonstrates the feasibility of combining transformer-based landmark detection with geometric ratios for strabismus screening. The framework shows high performance under controlled conditions. While the use of biometric ratios allows for feature-level inspection, further research is required to establish full clinical interpretability and performance in uncontrolled environments.