Version 1
: Received: 26 August 2024 / Approved: 27 August 2024 / Online: 28 August 2024 (10:33:01 CEST)
How to cite:
Diao, S.; Huang, S.; Wan, Y. Early Detection of Cervical Adenocarcinoma Using Immunohistochemical Staining Patterns Analyzed through Computer Vision Technology. Preprints2024, 2024082003. https://doi.org/10.20944/preprints202408.2003.v1
Diao, S.; Huang, S.; Wan, Y. Early Detection of Cervical Adenocarcinoma Using Immunohistochemical Staining Patterns Analyzed through Computer Vision Technology. Preprints 2024, 2024082003. https://doi.org/10.20944/preprints202408.2003.v1
Diao, S.; Huang, S.; Wan, Y. Early Detection of Cervical Adenocarcinoma Using Immunohistochemical Staining Patterns Analyzed through Computer Vision Technology. Preprints2024, 2024082003. https://doi.org/10.20944/preprints202408.2003.v1
APA Style
Diao, S., Huang, S., & Wan, Y. (2024). Early Detection of Cervical Adenocarcinoma Using Immunohistochemical Staining Patterns Analyzed through Computer Vision Technology. Preprints. https://doi.org/10.20944/preprints202408.2003.v1
Chicago/Turabian Style
Diao, S., Shijia Huang and Yajie Wan. 2024 "Early Detection of Cervical Adenocarcinoma Using Immunohistochemical Staining Patterns Analyzed through Computer Vision Technology" Preprints. https://doi.org/10.20944/preprints202408.2003.v1
Abstract
This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.
Keywords
Machine Learning; Ischemic Stroke; Functional Recovery Prediction; Big Data Analytics
Subject
Biology and Life Sciences, Ecology, Evolution, Behavior and Systematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.