![]() The relatively limited scale of datasets and lack of external validation were the limitations of most studies. The AI models have shown the potential to predict MSI with the highest AUC of 0.93 in the case of CRC. Colorectal cancer (CRC) was the most common type of cancer studied, followed by endometrial, gastric, and ovarian cancers. The commonly used dataset is The Cancer Genome Atlas dataset. The included 14 studies were published between 20, and most of the publications were from developed countries. Studies were searched in online databases and screened by reference type, and only the full texts of eligible studies were reviewed. Here, we aimed to assess the current state of AI application to predict MSI based on WSIs analysis in MSI-related cancers and suggest a better study design for future studies. Therefore, artificial intelligence (AI)-based models have been recently developed to evaluate MSI from whole slide images (WSIs). However, MSI is not tested in all cancers because of the additional costs and time of diagnosis. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.Ĭancers with high microsatellite instability (MSI-H) have a better prognosis and respond well to immunotherapy. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. ![]() Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.ĭigital pathology is revolutionizing pathology. ![]() (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. (New York City, NY, USA) and Siemens, Inc. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). Patent filing and publication have increased exponentially over the past five years. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees word cloud analysis and subject category analyses. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment.
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