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机器学习模型的比较,用于解释希尔施普朗格病中的透水溶液

P Vargová¹, A González Esgueda¹, R Fernández Atuan¹

¹Pediatric Surgery Department. Hospital Universitario Miguel Servet. Zaragoza (Spain).

Cirugia pediatrica : organo oficial de la Sociedad Espanola de Cirugia Pediatrica

|November 7, 2025

概括

机器学习模型在解释希尔施普朗格病 (HD) 的酸浸泡剂方面表现有前途,有助于诊断和排除该疾病. 这种人工智能支持可以提高准确性,特别是在专家放射科医生稀缺的地方.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
儿科胃肠病学儿科胃肠病学

背景情况:

希尔施普朗格病 (HD) 的诊断依赖于解释气囊.
准确的解释对于及时治疗至关重要.
在某些情况下,儿科放射学方面的专业知识可能是有限的.

研究的目的:

评估人工智能模型的诊断准确性,包括ChatGPT,用于解释HD的细阴道.
将AI性能与儿科放射科医生的性能进行比较.

主要方法:

追溯分析来自15岁以下患者的266个透水溶液.
训练和验证人工智能模型 (支持向量,后勤回归) 在阴道图像上.
在单独的测试组上使用灵敏度,特异性和ROC曲线进行性能评估.
与回顾性放射学报告和放射科医生的表现进行比较.

主要成果:

人工智能模型的分类能力与专家放射科医生的分类能力相当.
支持向量模型实现了72.7%的灵敏度和93%的特异性.
后勤回归模型显示AUC-ROC为0.73.
与回顾性报告相比,人工智能模型的灵敏度为81%,特异性为76%.

结论:

人工智能模型显示了通过水浸泡解释支持HD诊断的潜力.
人工智能表现出良好的能力,可以排除赫施普朗格病.
这项技术可以在资源有限的环境中提高诊断准确性.
对于临床优化,需要进一步的研究.

关键词:

人工智能的人工智能是人工智能.气囊是因为气囊.春病是春病的一种疾病.机器学习是机器学习.支持矢量机器的支持矢量机器.

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