人工智能病理学家:人工智能在数字医疗中的应用

Asmaa Ben Ali Kaddour, N. Abdulaziz
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引用次数: 0

摘要

人工智能正在给许多行业带来革命性的变化,将它们引入一个充满技术进步的新时代。通过将数字化转型与医疗保健相结合,形成数字医疗保健,医疗保健行业已成为这一变化的最大受益者之一。因此引入了数字病理学,它实现了图像处理算法,帮助病理学家更快、更有效地分析和检查诊断。它不仅减少了病理学家在实验室分析中花费的长时间,而且还减少了人为错误。因此,医疗保健数字化使得计算机视觉融入医疗领域,使用人工智能技术,如深度学习和机器学习算法。然而,过去的研究工作仅限于使用人工智能模型一次诊断一种特定疾病。而这项研究工作旨在开发一种人工智能模型,该模型将自动进行病理分析,从医学图像中确定多种疾病的诊断,然后提供医疗报告,同时保护患者的数据,并协助他们解决有关诊断的任何问题。本研究应用深度学习和机器学习算法通过CNN架构进行图像分类,并通过形态学属性进行特征提取。模型取得了很好的效果,准确率较高,f1评分结果分别为90.47%和0.8332分。由此产生的模型诊断了12种医学疾病,总共有29个诊断病例,使其成为数字化医疗应用中唯一的此类模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence Pathologist: The use of Artificial Intelligence in Digital Healthcare
Artificial intelligence is bringing revolutionary changes to so many industries, by introducing them to a new era, full of technological advancements. The healthcare industry has been one of the most beneficial to this change, by merging digital transformation and healthcare, to form digital healthcare. Thereby introducing digital pathology, which implements image processing algorithms to help pathologists analyze and examine a diagnosis faster and more efficiently. It not only reduces the long hours pathologists used to take in laboratory analysis but also reduces human error. Therefore, healthcare digitalization has allowed the integration of computer vision into the medical field, with the use of Artificial intelligence techniques such as deep learning and machine learning algorithms. However, past research work has been limited to using AI models to diagnosis one specific disease at a time. Whereas this research work aims to develop an AI model that will automatically perform pathological analysis, to determine the diagnosis for multiple diseases from a medical image, then provide the medical report, while securing the patient’s data, and assisting them with any questions they might have regarding the diagnosis. This research applies deep learning and machine learning algorithms for image classification via CNN architectures and feature extraction via Morphological properties. The model achieved great outcomes, with high accuracy and good F1-score results of 90.47% and 0.8332 respectively. The resultant model diagnoses 12 medical disorders, with an overall of 29 diagnostic cases, making it the only one of its kind in digitized healthcare applications.
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