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甜甜圈:增强技术来提高青光眼疑似查的有效性

S Sangchocanonta, S Ingpochai, S Puangarom

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

概括

"甜甜圈方法"通过提高人工智能训练的 fundus 图像质量来改善对玻璃眼的疑似查. 这种技术提高了检测青光眼疑似患者的灵敏度,有助于早期诊断和预防失明.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
医疗成像医学成像

背景情况:

青光眼是导致失明的主要原因,早期检测青光眼疑似患者至关重要.
评估可疑患有青光眼的患者存在诊断挑战.
高质量的 fundus 图像对于训练人工智能模型进行青光眼查至关重要.

研究的目的:

引入"甜甜圈方法"作为一种增强技术,以提高 fundus 图像质量.
为了提高使用ConvNeXt-Small模型对可疑青光瘤患者的分类效率.
用GradCAM++热图来验证拟议方法的临床相关性.

主要方法:

"甜甜圈方法"是为了增强 fundus 图像而开发的,专注于视神经头部区域.
来自GlauCUTU-DATA数据集的 fundus 图像被多位眼科医生标记.
ConvNeXt-Small模型使用增强图像进行训练,并使用灵敏度和特异性指标评估性能.

主要成果:

"甜甜圈方法"显著提高了检测可疑青光眼患者的灵敏度.
灵敏度从52.94%提高到70.59% (3/3协议) 和37.78%提高到42.22% (2/3协议).
GradCAM++热图证实,该方法突出了对青光眼诊断的临床相关特征.

结论:

"甜甜圈方法"通过改善眼底图像质量,有效地提高了对眼病嫌疑人的分类.
这种增强技术可以提高AI模型在玻璃眼查中的性能.
该方法有助于早期发现青光眼,可能减少失明率.

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