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ACOMS+ 및 학술지 리포지터리 설명회

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

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Application of Artificial Intelligence in Oral Pathology

Abstract

Recently, with the development of information science and technology, data has exploded in various fields, and big data is being analyzed and interpreted in each field and widely used. Although the use of artificial intelligence (AI) in oral pathology is still in its infancy, many attempts are being made to diagnose and evaluate through automated image analysis after using a slide scanner to store the whole histopathologic slides as a digital image. In particular, in the field of oral cancer diagnosis, attempts are being made to increase accuracy by adding meaningful information such as clinical characteristics and radiologic images to these digital images to enable more accurate diagnosis through analysis and processing. In this paper, we will discuss the current uses, limitations, and future roles of AI in oral pathology.

keywords
Oral pathology, artificial intelligence (AI), oral cancer

참고문헌

1.

1. Panayides, A. S., Amini, A., Filipovic, N. D. et al. AI and medical imaging informatics: current challenges and future directions. IEEE J. Biomed. Health Inform. 2020; 24,1837–1857.

2.

2. Krishnan, M. M., Venkatraghavan, V., Acharya, U. R. et al. Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. Micron 2012; 43,352–364.

3.

3. L u, C., Lewis, J. S., Dupont, W. D. et al. An oarl cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for diseasespecific survival. Mod. Pathol. 2017; 30,1655–1665.

4.

4. Das, D. K., Bose, S., Maiti, A. K. et al. Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis. Tissue Cell 2018; 53,111–119.

5.

5. Somashekhar SP, Sepúlveda MJ, Puglielli S et al. Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol. 2018;29(2):418–423.

6.

6. Yu KH, Zhang C, Berry GJ et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016; 7:12474.

7.

7. Kumar N, Tafe LJ, Higgins JH et al. Identifying associations between somatic mutations and clinicopathologic findings in lung cancer pathology reports. Methods Inf Med. 2018; 57(1):63–73.

8.

8. Ashizawa, K., Yoshimura, K., Johno, H. et al. Construction of mass spectra database and diagnosis algorithm for head and neck squamous cell carcinoma. Oral. Oncol. 2017; 75,111–119.

9.

9. Liu, Y., Li, J., Liu, X. et al. Quantitative risk stratification of oral leukoplakia with exfoliative cytology. PLoS ONE 2015; 10, e0126760.

10.

10. Song, B., Sunny, S., Uthoff, R. D. et al. Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning. Biomed. Opt. Express 2018; 9,5318–5329.

11.

11. Jeyaraj, P. R. & Nadar, E. R. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J. Cancer Res. Clin. Oncol. 2019; 145,829–837.

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