Preliminary application of artificial intelligence in the pathological diagnosis of ameloblastoma

doi:10.19438/j.cjoms.2025.02.004

Abstract

Abstract: PURPOSE: To investigate the effect of artificial intelligence in the pathological diagnosis of ameloblastoma, and to preliminarily explore the value of artificial intelligence in the field of oral pathology. METHODS: The pathological images of 90 cases of ameloblastoma were used as the research objects, and the U-net-like structure neural network was constructed. The 90 H-E images of ameloblastoma were divided into a training set (72 images), a validation set (9 images) and a test set (9 images) for training and testing the model respectively. The mIoU and ROC curve were used to evaluate the ability of the U-net network model in the identification of ameloblastoma epithelium. RESULTS: The mIoU of negative area segmented by U-net model was 0.818 and the positive area was 0.846. The area under the ROC curve was 0.92. CONCLUSIONS: The U-net network model has a good segmentation for the positive and negative regions of ameloblastoma, and can distinguish between negative and positive sections. It can be preliminarily applied to the pathological diagnosis of ameloblastoma, and is expected to be gradually popularized in clinical practice after further validation with large samples.

Key words: Ameloblastoma, Pathological diagnosis, Artificial intelligence, Oral pathology, U-net network, mIoU, ROC curve

CLC Number:

R739.8

QIAO Xin-wei, LI Mao, SHEN Ze-liang, ZHANG Lin-han, ZHENG Zhi-jian, TANG Ya-ling. Preliminary application of artificial intelligence in the pathological diagnosis of ameloblastoma[J]. China Journal of Oral and Maxillofacial Surgery, 2025, 23(2): 122-128.

References

[1] Liu W, Zheng C, Zhang X, et al.Recurrence and malignant risk of ameloblastoma: a demographic study of 1 626 cases from east China[J]. Oral Oncol, 2024, 148: 106651.
[2] Vila S, Oster RA, James S, et al. A retrospective analysis of 129 ameloblastoma cases: clinical and demographical trends from a single institution[J]. J Racial Ethn Health Disparities, 2024 Apr 12:10.1007/s40615-024-01993-3.[Online ahead of print]
[3] Sakamoto K, Morita KI, Ikeda T, et al.Deep-learning-based identification of odontogenic keratocysts in hematoxylin- and eosin-stained jaw cyst specimens[J]. arXiv preprint arXiv:1901.03857, 2019.
[4] Rao RS, Shivanna DB, Mahadevpur KS, et al.Deep learning-based microscopic diagnosis of odontogenic keratocysts and non-keratocysts in haematoxylin and eosin-stained incisional biopsies[J]. Diagnostics (Basel), 2021, 11(12): 2184.
[5] 于颖彦. 人工智能在病理学研究与应用的进展[J]. 中华病理学杂志, 2020, 49(5): 393-396.
Yu YY.Progress of artificial intelligence on pathological research and utilization[J]. Chinese Journal of Pathology, 2020, 49(5): 393-396.
[6] Dai D, Dong C, Yan Q, et al.I2U-Net: a dual-path U-Net with rich information interaction for medical image segmentation[J]. Med Image Anal, 2024, 97: 103241.
[7] Almalki SA, Alsubai S, Alqahtani A, et al.Denoised encoder-based residual U-net for precise teeth image segmentation and damage prediction on panoramic radiographs[J]. J Dent, 2023, 137: 104651.
[8] 吴毅, 张小勤. 人工智能在医学图像处理中的研究进展与展望[J]. 第三军医大学学报, 2021, 43(18): 1707-1712.
Wu Y, Zhang XQ.Artificial intelligence in medical image processing: progress and prospect[J]. Journal of Third Military Medical University, 2021, 43(18): 1707-1712.
[9] 郝逸航,黄美畅,李茂,等.人工智能在根尖周囊肿病理诊断中的初步应用[J].口腔疾病防治, 2023, 31(9): 641-646.
Hao YH, Huang MC, Li M, et al.Preliminary application of artificial intelligence in the pathological diagnosis of periapical cysts[J]. Journal of Prevention and Treatment for Stomatological Diseases, 2023, 31(9): 641-646.
[10] Lee A, Kim MS, Han SS, et al.Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography[J]. PLoS One, 2021, 16(7): e0254997.
[11] 吴运堂,于世凤,张祖燕,等.壁性成釉细胞瘤术后追踪观察[J].中华口腔医学杂志,2001,36(1):67-69.
Wu YT, Yu SF, Zhang ZY, et al.Postoperative follow-up of mural ameloblastoma[J]. Chinese Journal of Stomatology, 2001,36(1):67-69.
[12] Yang H, Jo E, Kim HJ, et al.Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs[J]. J Clin Med, 2020, 9(6):1839.
[13] Curi MM, Dib LL, Pinto DS.Management of solid ameloblastoma of the jaws with liquid nitrogen spray cryosurgery[J]. Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 1997, 84(4): 339-344.
[14] Liu Z, Liu J, Zhou Z, et al.Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs[J]. Int J Comput Assist Radiol Surg, 2021, 16(3): 415-422.
[15] Chai ZK, Mao L, Chen H, et al.Improved diagnostic accuracy of ameloblastoma and odontogenic keratocyst on cone-beam CT by artificial intelligence[J]. Front Oncol, 2021, 11: 793417.
[16] Cai X, Zhang H, Wang Y, et al.Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts[J]. Int J Oral Sci, 2024, 16(1): 16.
[17] Ciresan DC, Giusti A, Gambardella LM, et al.Mitosis detection in breast cancer histology images with deep neural networks[J]. Med Image Comput Comput Assist Interv, 2013, 16(Pt 2): 411-418.
[18] Fu B, Peng Y, He J, et al.HmsU-Net: a hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation[J]. Comput Biol Med, 2024, 170: 108013.