Abstract
Nowadays, digital pathology has revolutionized the traditional mode of pathology work and has brought about significant advancements in the field. This technology provides a stronger foundation for accurate diagnosis and serves as an inevitable trend in the development of pathology. With the advancement of computer and network technology, digital pathology has undergone three stages of development. It has evolved from digital scanning equipment to a pathological cloud platform and now includes artificial intelligence-aided diagnosis. These advancements have consistently driven the new development of accurate diagnosis. Digital pathology is being increasingly used in clinics. Currently, the three primary applications in the field of pathology are pathology department management, remote pathological consultation, and pathological artificial intelligence. Among these, remote pathological consultation is the most well-established field, as it enables standardized management of pathology departments, significantly reduces the workload of pathologists, improves the accuracy and efficiency of clinical pathological diagnosis, and enhances the level of pathological diagnosis in primary hospi-tals. Digital pathology is a relatively new technology, but there are still some challenges that need to be addressed and continuously improved upon. This paper discusses the development stage, application, and challenges of digital pathology, and presents future development prospects and opportunities.
References
[1] Chen Xiaozhi, Wang Jiangang. Application and development of digital pathology and artificial intelligence in cases [J]. Henan Medical Research, 2020, 29 (3): 419-422.
[2] Ding Xie, Liu Ming, Zhang Chuanguo. Wanda Information Co., Ltd. [J]. China Digital Medicine, 2022, 17 (1) 83-88.
[3] Bao Ji, Bu Hong. Development prospect of digital pathology in China [J]. practical journal of clinical medicine, 2017, 14 (5): 1-2.
[4] Yu Jingchun, Guo Mingxing, Han Jing, et al. Progress of artificial intelligence in pathological diagnosis [J]. Journal of Molecular Imaging, 2022, 45 (5): 779-789.
[5] Retamero J A, Aneiros-Fernandez J, Moral R G D. Complete digital pathology for routine histopathology diagnosis in a multicenter hospital network. Arch Pathol Lab Med, 2020, 144(2):221-228.
[6] Chen Yuxi. Solution of regional pathology remote consultation platform [J]. Fujian Computer, 2019, 35 (6): 37-39.
[7] Pan Guoqing, Su Guomiao, Wang Kunhua. The effect of remote consultation on pathology in basic pathology department [J]. Clinical Medical Research and Practice, 2020, 5 (36): 20-21.
[8] Liang Li. Investigation report on remote pathological diagnosis in China. Chinese Journal of Pathology, 2020, 49 (6): 533-535.
[9] Bera K, Schalper K A, Rimm D L, et al. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 2019, 16(3).
[10] Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zllner FG. Multi-class texture analysis in colorectal cancer histology. Sci Rep. 2016 Jun 16; 6:27988. doi: 10.1038/srep27988. PMID: 27306927; PMCID: PMC4910082.
[11] Hu Rong, Zhong Qi, Xu Wen et al. Application of artificial intelligence based on deep convolution neural network in narrow-band imaging-assisted diagnosis of laryngeal squamous cell carcinoma [J]. Chinese Journal of Otolaryngology Head and Neck Surgery, 2021, (5):454-458.