论文标题
AI支持工具的开发和临床评估，用于改善远程医疗质量
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
论文作者
论文摘要
远程医疗利用率在19009年大流行期间加速了，皮肤条件是常见的用例。但是，患者发送的照片的质量仍然是一个主要限制。为了解决这个问题，我们开发了TrueImage 2.0，这是一种人工智能（AI）模型，用于评估远程医疗的患者照片质量，并向患者提供实时反馈以改善照片质量。 TrueImage 2.0接受了临床医生注释的1700张远程医疗图像的培训。在357个远程医疗图像的回顾性数据集中，TrueImage 2.0有效地识别了质量差的图像（接收器操作员曲线曲线（ROC-AUC）= 0.78）和质量差的原因（ROC-AUC模糊= 0.84，照明问题ROC-AUC = 0.70）。在年龄，性别和肤色之间的表现是一致的。接下来，我们评估了患者真实性2.0相互作用是否通过对98名患者的前瞻性临床试验研究改善了提交的照片质量。 TrueImage 2.0将质量不佳的患者数量减少了68.0％。
Telemedicine utilization was accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, the quality of photographs sent by patients remains a major limitation. To address this issue, we developed TrueImage 2.0, an artificial intelligence (AI) model for assessing patient photo quality for telemedicine and providing real-time feedback to patients for photo quality improvement. TrueImage 2.0 was trained on 1700 telemedicine images annotated by clinicians for photo quality. On a retrospective dataset of 357 telemedicine images, TrueImage 2.0 effectively identified poor quality images (Receiver operator curve area under the curve (ROC-AUC) =0.78) and the reason for poor quality (Blurry ROC-AUC=0.84, Lighting issues ROC-AUC=0.70). The performance is consistent across age, gender, and skin tone. Next, we assessed whether patient-TrueImage 2.0 interaction led to an improvement in submitted photo quality through a prospective clinical pilot study with 98 patients. TrueImage 2.0 reduced the number of patients with a poor-quality image by 68.0%.
