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dc.contributor.advisorSitanggang, Maria Novita Helen
dc.contributor.authorAsoga, Sharveen Rao
dc.date.accessioned2026-01-07T04:18:29Z
dc.date.available2026-01-07T04:18:29Z
dc.date.issued2025
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/111904
dc.description.abstractSensitivity and Specificity of Malocclusion Classification Reviewed from Cephalometric Radiographs Using YOLO V4 Software xiii + 40 pages Lateral cephalometric radiography plays a crucial role in the analysis of skeletal relationships; however, manual measurements are prone to operator subjectivity and are relatively time-consuming. Advances in artificial intelligence, particularly You Only Look Once version 4 (YOLO v4), have the potential to improve the accuracy and efficiency of cephalometric analysis. This study aimed to determine the mean values of SNA, SNB, and ANB angles in Class I, II, and III malocclusions measured using YOLO v4 software and manual cephalometric analysis, as well as to evaluate the sensitivity and specificity of YOLO v4. This analytical cross-sectional study involved 330 lateral cephalometric radiographs of patients aged 20–30 years with malocclusion. Angular measurements were performed using manual cephalometric analysis and YOLO v4 software. The results showed no significant differences in the mean SNA, SNB, and ANB values obtained using YOLO v4 compared with manual cephalometric analysis across all malocclusion classes (p > 0.05). The YOLO v4 software demonstrated a mean specificity of 0.7688 and an accuracy of 100%. It can be concluded that YOLO v4 exhibits good sensitivity and specificity, with measurement outcomes comparable to manual cephalometric analysis. Therefore, YOLO v4 has the potential to be used as an efficient and objective diagnostic aid for malocclusion assessment in orthodontic practiceen_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectKlasifikasi maloklusien_US
dc.subjectRadiograf sefalometriken_US
dc.subjectYOLO v4en_US
dc.subjectDeteksi objeken_US
dc.subjectDiagnosis ortodontiken_US
dc.titleSensitivitas dan Spesifisitas Klasifikasi Maloklusi Ditinjau dari Radiograf Sefalometri Menggunakan Software Yolo V4en_US
dc.title.alternativeSensitivity and Specificity of Malocclusion Classification Reviewed from Cephalometric Radiographs Using Yolo V4 Softwareen_US
dc.typeThesisen_US
dc.identifier.nimNIM200600246
dc.identifier.nidnNIDN0005117902
dc.identifier.kodeprodiKODEPRODI12201#Pendidikan Dokter Gigi
dc.description.pages69 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US
dc.subject.sdgsSDGs 4. Quality Educationen_US


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