PERKEMBANGAN PENGGUNAAN INTELIGENSIA BUATAN SEBAGAI ALAT BANTU DIAGNOSA PENYAKIT PERIODONTITIS BERBASIS DATA CITRA RONTGEN PANORAMIK GIGI: KAJIAN PUSTAKA
Bibliographic record
Abstract
Kemajuan dalam bidang Inteligensia Buatan (Artificial Intelligence, AI) telah menghadirkan peluang baru dalam dunia medis, khususnya dalam analisis citra medis berbasis prinsip fisika. Penyakit periodontitis, yang merupakan infeksi kronis pada jaringan penyangga gigi, dapat dideteksi melalui pencitraan rontgen panoramik yang memanfaatkan sifat penyerapan dan hamburan sinar-X oleh struktur anatomi gigi dan tulang alveolar. Namun, analisis citra ini sering kali bergantung pada subjektivitas dokter gigi, sehingga diperlukan pendekatan komputasional untuk meningkatkan akurasi deteksi. Kajian pustaka ini bertujuan untuk mengidentifikasi metode AI yang diterapkan dalam proses diagnosis periodontitis menggunakan citra rontgen panoramik gigi. Delapan artikel ilmiah yang direview menunjukkan penggunaan teknik ekstraksi fitur seperti Gray Level Co-occurrence Matrix (GLCM) dan algoritma klasifikasi meliputi Random Forest, k-Nearest Neighbours (kNN), Convolutional Neural Network (CNN), YOLOv4, InceptionV3, dan Faster R-CNN. Evaluasi performa menghasilkan akurasi antara 64% hingga 91% dan nilai F1-score tertinggi sebesar 91,07%. Validitas metodologi pada artikel dinilai menggunakan Newcastle-Ottawa Scale (NOS) dan mayoritas menunjukkan kualitas sedang hingga tinggi. Temuan ini memperlihatkan bahwa integrasi AI dalam analisis radiografi gigi berpotensi memberikan hasil diagnosis yang lebih objektif dan efisien..
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".