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人工智能与医生对比:在腹部X射线图上诊断死性肠球炎
Jennine H Weller1, Daniel Scheese1, Cody Tragesser1
1Division of Pediatric Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
一个深层卷积神经网络 (DCNN) 准确地识别了新生儿的死性肠球炎 (NEC),其表现与高级外科住院医生的表现相当. 这种AI工具可以帮助临床医生及时诊断NEC.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 新生儿手术 新生儿手术
背景情况:
- 通过放射学诊断死性肠球炎 (NEC) 是一个挑战.
- 深度学习模型通过检测微妙的成像模式来提高诊断准确性的潜力.
- 这项研究旨在比较DCNN与高级外科住院医生的诊断性能.
研究的目的:
- 为了评估深层卷积神经网络 (DCNN) 的准确性,从新生儿腹部放射图中诊断死性肠球炎 (NEC).
- 将DCNN的表现与高级外科住院医生在识别肺炎方面的表现进行比较,这是NEC的关键指标.
- 评估AI在协助临床实践中的潜力,以便快速识别NEC.
主要方法:
- 使用了一组494张新生儿腹部X射线图,其中214张图像显示NEC.
- 一个ResNet-50 DCNN在训练/验证/测试集上使用转移学习进行了微调.
- 使用AUROC评估DCNN的表现,并使用DeLong的方法与高级外科住院医生进行比较.
主要成果:
- 在DCNN实现了AUROC的0.918和87.8%的准确性在识别肺炎.
- 格拉德-CAM热图显示DCNN专注于相关的图像区域.
- 该DCNN的表现与高级外科住院医生的表现相当 (p > 0.05).
结论:
- 经过 pneumatosis 识别培训的 DCNN 可以有效地协助临床医生快速诊断 NEC.
- 人工智能模型的准确性与经验丰富的外科住院医生相美.
- 这项技术为改善临床环境中NEC检测提供了一个有前途的工具.
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