Oral diseases such as oral cancer, precancerous lesions, infections, and oral tissue disorders remain serious challenges in dentistry and public health. These conditions are often detected at an advanced stage because early symptoms are difficult to recognise. Therefore, innovative diagnostic technologies are critically important for early, rapid, and non-invasive screening.
One of the latest innovations comes from the UGM Student Creativity Program – Product Development (PKM-KC) Research Team, consisting of Heironymus Damar Jati Danisworo, Aurelius Galih Arkananta, Hikmat Sejati, and Fatimah Islamia, who developed a rapid detection device for oral cancer called Orside: A Portable Fluorescence-Based Precancerous Lesion Detector with Deep Learning Convolutional Neural Network (CNN). This device is designed to detect abnormal oral lesions more quickly and accurately at the point of care.
Modern Diagnostic Technologies in Oral Diseases
Several advanced diagnostic approaches and technologies currently used or under development in the field of oral diseases include:
- Optical Fluorescence and Autoluminescence
This method utilises the different properties of healthy and abnormal tissues in emitting or absorbing light at specific wavelengths. The Orside device uses blue fluorescence light to differentiate between healthy and abnormal tissues in the oral cavity. - Artificial Intelligence (AI) / Deep Learning / CNN
Images obtained from scanning are processed using deep learning algorithms (CNN) to recognise micro-patterns that are not visible to the human eye. Orside applies CNN for image analysis and prediction of precancerous lesions based on fluorescence results. - Multimodal Imaging (Optical + Spectroscopy)
The combination of techniques such as fluorescence, spectroscopy, and optical reflectance can provide additional information on tissue biochemical content, helping to distinguish normal from pathological tissues. - 3D Imaging and Cone Beam / CT / CBCT Imaging
For the diagnosis of structural abnormalities, tumors, and surgical planning, CBCT and CT technologies are used to accurately visualise bone structures and hard/soft tissues of the oral cavity. - Mobile Imaging / Tele-diagnosis / Smartphone Applications
The use of smartphone cameras with special lenses, supported by AI algorithms, enables patients or primary healthcare providers to perform initial self-screening or screening in basic healthcare facilities.
Advantages and Benefits of Diagnostic Technologies Such as Orside
- Early detection: Devices like Orside enable precancerous lesions to be identified before severe symptoms appear, allowing earlier intervention and better prognosis.
- Fast and non-invasive: Compared to biopsy, this method is more comfortable for patients, does not require minor surgical procedures, and provides faster results.
- Portable / point-of-care: Can be used directly in clinics or primary care settings without large equipment.
- Digital integration and data storage: Results can be stored in the cloud and accessed in real time by healthcare professionals for verification and follow-up.
Challenges and Key Considerations
- Accuracy and clinical validation: New technologies must undergo clinical trials to demonstrate sensitivity, specificity, and reliability in large populations.
- Artifacts and tissue variation: Differences in oral anatomy, tissue pigmentation, and the presence of metal restorations may affect fluorescence imaging results.
- Limited light penetration: Fluorescence techniques are more effective for surface tissues; deep lesions or those behind dense structures may be difficult to detect.
- Digital infrastructure requirements: Cloud connectivity, AI software, and data processing systems require strong technological support and health data regulations.
- Cost and large-scale adoption: To be widely used in primary care or resource-limited settings, devices must be designed to be affordable.
References
Tim Riset Program Kreativitas Mahasiswa Karya Cipta (PKM-KC) UGM, Heironymus Damar Jati Danisworo, Aurelius Galih Arkananta, Hikmat Sejati, Fatimah Islamia, Jumlah Penderita Kanker Mulut Meningkat, Mahasiswa UGM Kembangkan Alat Deteksi Cepat, https://ugm.ac.id/id/berita/jumlah-penderita-kanker-mulut-meningkat-mahasiswa-ugm-kembangkan-alat-deteksi-cepat/
Author: Rizky B. Hendrawan | Photo: Freepik
