糖尿病足部的鉴定:一篇评论
Sujit Kumar Das1, Pinki Roy2, Prabhishek Singh3
1Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan University, Bhubaneswar 751030, India.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
糖尿病足 (DFU) 诊断可以使用机器学习 (ML) 和深度学习 (DL) 计算机视觉技术来改进. 卷积神经网络 (CNN) 显示出在临床实践中更快,更可靠地识别DFU的重大前景.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 糖尿病是一种慢性疾病,其特点是血糖水平不受控制.
- 糖尿病足 (DFU) 是一种严重的并发症,可能导致肢体截肢.
- 目前的DFU诊断涉及复杂,耗时和昂贵的临床程序.
研究的目的:
- 审查糖尿病足自动识别技术的现状.
- 为研究人员提供机器学习和深度学习应用在DFU诊断中的概述.
- 突出计算机视觉方法改善DFU检测的潜力.
主要方法:
- 审查现有的关于机器学习 (ML) 和深度学习 (DL) 的文献,以确定DFU.
- 分析使用图像特征的传统ML方法.
- 评估先进的DL技术,特别是卷积神经网络 (CNN).
主要成果:
- 传统的ML方法利用图像特征进行准确的DFU识别.
- 先进的DL方法,特别是CNN,与传统ML相比,表现优越.
- 使用人工智能的自动DFU识别显示了更快,更可靠的临床决策的潜力.
结论:
- 传统的ML和先进的DL技术都对提高DFU诊断至关重要.
- 基于CNN的解决方案正在主导DFU自动识别领域.
- 这一审查有助于研究人员了解当前的情况,并确定未来的DFU检测研究方向.
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