Performa Diagnostik Artificial Intelligence untuk Deteksi Osteoporosis pada Radiograf Panoramik
Shinta Wisnu Ariyani, Dr. drg. Rini Widyaningrum, M. Biotech ; drg. Silviana Farrah Diba, Sp.RKG
2023 | Skripsi | PENDIDIKAN DOKTER GIGI
Deteksi osteoporosis
dapat dilakukan menggunakan pengamatan visual dan pengukuran manual pada
radiograf panoramik. Deteksi manual menunjukkan kesesuaian dengan hasil
pemeriksaan Dual X-ray Absorptiometry (DEXA), namun ditemukan kelemahan
pada hasil interpretasi seperti subjektivitas dan rentang waktu yang lama. Oleh
karena itu, deteksi osteoporosis menggunakan radiografi panoramik dari manual
berkembang menjadi otomatis dengan tujuan membantu memperbaiki akurasi dan
efisiensi dalam deteksi osteoporosis. Pemanfaatan artificial intelligence (AI)
untuk deteksi osteoporosis pada radiograf panoramik telah dikembangkan. Ketepatan
sebuah algoritma dalam mendeteksi osteoporosis dapat diketahui dari performa masing-masing
AI. Performa AI diukur melalui sensitivitas, spesifitas, dan akurasi.
Review
dilakukan dengan mencari literatur dari tiga database yang dapat diakses melalui electronic resources
(e-resources) antara lain Google Scholar, ScienceDirect, dan PubMed.
Pencarian literatur menggunakan kata kunci osteoporosis, orthopantomograph,
dental panoramic radiograph, automatic, dan automatic
detection. Pencarian literatur dibatasi oleh kriteria inklusi berupa tahun
terbit 2014 hingga 2023 yang menggunakan bahasa Indonesia atau bahasa Inggris.
Hasil review terhadap 12 artikel utama menunjukkan
bahwa performa diagnostik AI untuk deteksi osteoporosis pada radiograf
panoramik menunjukkan hasil yang hampir menyerupai diagnosis osteoporosis
berdasarkan DEXA, dilihat dengan nilai sensitivitas, spesifisitas, dan akurasi masing-masing
AI mencapai 48,6-100%, 26,3-100%, dan 53,9-99,3%. Pencapaian performa diagnostik yang optimal
perlu memperhatikan karakteristik sampel, region of interest (RoI),
fitur, dan algoritma yang digunakan. Pengembangan AI sebagai alat diagnostik
osteoporosis pada radiograf panoramik menunjukkan potensi yang besar pada masa
mendatang dan memerlukan perhatian yang cermat serta kerjasama secara berkelanjutan
lintas bidang antara klinisi dan ahli komputasi.
Osteoporosis
detection can be done using visual observation and manual measurements on panoramic
radiographs. The manual detection has shown alignment with Dual X-ray Absorptiometry
(DEXA) examination results. However, weaknesses were found in the
interpretation results, such as subjectivity and time-consuming. Therefore, the
application of panoramic radiography has been developed into an automated
system to assist in improving the accuracy and efficiency of osteoporosis
detection. The utilization of artificial intelligence (AI) in osteoporosis
detection on panoramic radiographs has been developed. The accuracy of an
algorithm in osteoporosis detection can be determined by their respective
performance. Performance is measured through sensitivity, specificity, and
accuracy.
A
review was conducted by searching literature from three electronic resource
databases, namely Google Scholar, ScienceDirect, and PubMed. The literature was
searched using keywords such as osteoporosis, orthopantomograph, dental
panoramic radiograph, automatic, and automatic detection. The literature search
was limited by inclusion criteria of publications from 2014 to 2023 in either
Indonesian or English language.
The results of the review of 12 main articles
showed that the diagnostic performance of AI for osteoporosis detection on
panoramic radiographs showed results that were almost similar to the diagnosis
of osteoporosis based on DEXA, as seen from the sensitivity, specificity, and
accuracy values ranging from 48,6-100%, 26,3-100%, and 53,9-99,3% respectively.
Achieving optimal diagnostic performance requires attention to sample
characteristics, region of interest (RoI), features, and algorithms used. The
development of AI as a diagnostic tool for osteoporosis on panoramic
radiographs shows great potential in the future and requires careful attention
and sustainable multidiscipline collaboration between clinicians and
computational experts.
Kata Kunci : osteoporosis, diagnosis, radiograf panoramik, artificial intelligence, performa