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All Journal Dentino: Jurnal Kedokteran Gigi Indonesian Applied Physics Letters
Putra, Ayub Manggala
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Chemical Engineering, Chemistry & Bioengineering Dentistry Electrical & Electronics Engineering Materials Science & Nanotechnology Physics

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Review of Application YOLOv8 in Medical Imaging Widayani, Aisyah; Putra, Ayub Manggala; Maghriebi, Agiel Ridlo; Adi, Dea Zalfa Cahyla; Ridho, Moh. Hilmy Faishal
Indonesian Applied Physics Letters Vol. 5 No. 2 (2024): Indonesian Applied Physics Letters - December 2024
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v5i1.57001

Abstract

Deep learning has revolutionized medical imaging analysis, with YOLOv8 emerging as a promising tool forvarious tasks like lesion detection, organ segmentation and disease classification. This review investigates YOLOv8'sapplications across diverse medical imaging modalities (X-Ray, CT-Scan and MRI). We conducted a systematic literaturesearch across databases like Pubmed, ScienceDirect and IEEE to identify relevant studies evaluating YOLOv8'sperformance in medical imaging analysis. YOLOv8 achieved high performance for meningioma and pituitary tumorswith and without data augmentation (precision >0.92, recall >0.90, mAP >0.93). Glioma detection showed lowerperformance but still promising results (precision >0.86, recall >0.81, mAP >0.86). Breast cancer detection with SGDoptimizer yielded best performance with an average mAP of 0.87 for mass detection. The model achieved high accuracyin detecting normal (mAP 0.939) and malignant lesions (mAP 0.911). YOLO v8 on Dental radiograph successfullydetected cavities, impacted teeth, fillings and implants (precision of >0.82, recall of >0.78 and F1-Score of >0.80). Lastly,for lung disease classification, YOLOv8 achieved high accuracy (99.8% training and 90% validation) in classifyingnormal, COVID-19, influenza and lung cancer disease. With the importance to improve clinical decision-making andpatient outcomes in healthcare, the YOLOv8 algorthm underscores the importance of pre-processing, augmentation andoptimization of key hyperparameters.
DESCRIPTIVE STUDY OF RADIATION DOSE IN CBCT FOR DENTAL APPLICATION Sari, Amillia Kartika; Rahmawati, Lusiana Fitri; Pramono, Pramono; Putra, Ayub Manggala; ‘Aisy Farhah, Ghinaa Rihadatul; Palupi, Endah Sekar
Dentino: Jurnal Kedokteran Gigi Vol 11, No 1 (2026)
Publisher : FKG ULM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/dentino.v11i1.26067

Abstract

Background: Cone Beam Computed Tomography (CBCT) provides superior 3D imaging for dental diagnosis but involves higher radiation exposure than conventional radiography. Therefore, dose optimization based on the ALARA principle and the establishment of Diagnostic Reference Levels (DRLs) are essential for patient safety and radiation protection. Purpose: This study evaluated CBCT exposure parameters and radiation dose variation by FOV to support dose optimization and DRL development. Methods: This retrospective descriptive study analyzed CBCT records of adult patients (September 2023–2024). Variables included tube voltage, tube current, exposure time, voxel size, DAP, Effective Dose, and FOV. Data were analyzed using descriptive statistics and FOV-based comparisons with SPSS version 30. Results: A total of 50 patients were included (56% female and 44% male) had average exposure parameters of 89.80 kV, 2.87 mA, 15 seconds, and 0.148 mm voxel size. The 8x5 cm FOV (4 patients) produced a DAP of 721.50 µGy cm² and an Effective Dose of 70 µSv. The 8x9 cm FOV (46 patients) produced a DAP of 1151.48 µGy cm² and an Effective Dose of 72 µSv. The typical dose value (Q2 DAP) was 1179 µGy cm². Conclusion: Radiation doses in dental CBCT examinations vary depending on the FOV size, with larger FOVs resulting in higher radiation exposure. Selecting the appropriate FOV based on clinical indications, along with dose monitoring and establishing Diagnostic Reference Levels (DRLs), is crucial for optimizing patient safety and radiation protection in dental radiology. Keywords: Cone Beam Computed Tomography, DRL, Patient Safety, Radiation Protection, TDV
Co-Authors Adi, Dea Zalfa Cahyla Amillia Kartika Sari Maghriebi, Agiel Ridlo Palupi, Endah Sekar Pramono Pramono Rahmawati, Lusiana Fitri Ridho, Moh. Hilmy Faishal Widayani, Aisyah ‘Aisy Farhah, Ghinaa Rihadatul