바로가기메뉴

본문 바로가기 주메뉴 바로가기

ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

logo

메뉴

영상치의학에서의 인공지능 기술 동향

Trend of Artificial Intelligence technology in oral and maxillofacial radiology

Abstract

Artificial intelligence (AI) technology is widely used in our life. Likewise, AI in the field of oral and maxillo facial radiology is being studied in various fields such as detection, classification, segmentation, measurement, and image translation. This study aimed to investigate the 1) research papers and 2) developed software based on AI in the oral and maxillofacial radiology to help dentists actively utilize the upcoming AI application. Papers were searched through 5 major data bases until June 2021, and a total of 101 papers corresponding to dental imaging-based AI research in the oral and maxillofacial area were collected. A number of AI papers showed a sharp increase from 2017, and the most research conducted on panoramas. The AI based dental imaging software search for the purpose of commercialization was first collected based on the list announced on October 21, 2020 by the Ministry of Food and Drug Safety of Korea and the web da tabase search was on October 22-23, 2021. Total 23 software were searched, and the function of automatically detecting lesions in panoramic or apical images and the orthodontic diagnosis analysis function by automati cally recognizing important anatomical landmarks in cephalogram were the most popular in the world. If a clear profitability model is presented in the future and evidence for clinical effectiveness and ethical re sponsibility are prepared, the clinical use of AI-based application using dental images will increase. Therefore, it is thought that dentists need to take an interest in AI technology research trends and product development and actively participate.

keywords
Artificial intelligence, Oral and maxillofacial radiology, Deep learning

참고문헌

1.

1. Turing AM. Computing machinery and intelligence: Parsing the turing test: Springer; 2009: 23-65.

2.

2. 박성호. 의료인공지능: 인공지능 초심자를 위한 길라잡이. Journal of the Korean Society of Radiology. 2018; 78: 301-8.

3.

3. Geoff Hinton: On Radiology. YouTube 2016. https://www.youtube.com/watch?v=2HMPRXstSvQ (accessed February 4, 2022).

4.

4. Amisha PM, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. Journal of family medicine and primary care. 2019; 8: 2328.

5.

5. McGovern A, Lagerquist R, Gagne DJ, Jergensen GE, Elmore KL, Homeyer CR, Smith T. Making the black box more transparent: Understanding the physical implications of machine learning. Bulletin of the American Meteorological Society. 2019; 100: 2175-99.

6.

6. Kim YH, Lee C, Ha E-G, Choi YJ, Han S-S. A fully deep learning model for the automatic identification of cephalometric landmarks. Imaging Science in Dentistry. 2021; 51: 299.

7.

7. Lee A, Kim MS, Han S-S, Park P, Lee C, Yun JP. Deep learning neural networks to differentiate Stafne’s bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography. Plos one. 2021; 16: e0254997.

8.

8. Ha E-G, Jeon KJ, Kim YH, Kim JY, Han S-S. Automatic detection of mesiodens on panoramic radiographs using artificial intelligence. Scientific reports. 2021; 11:23061.

9.

9. 한국보건산업진흥원, 보건산업브리프 Vol.328 혁신성에 근거한 디지털헬스케어의 가치 평가 필요성, 2021-07-22.

10.

10. 식품의약품안전처 보도자료, 식약처, 다가올 의료 인공지능(AI) 미래를준비한다, 2020.10.21.

logo