PDF(Pubmed)
Abstract：
UNASSIGNED: Meticulous postoperative flap monitoring is essential for preventing flap failure and achieving optimal results in free flap operations, for which physical examination has remained the criterion standard. Despite the high reliability of physical examination, the requirement of excessive use of clinician time has been considered a main drawback.
UNASSIGNED: To develop an automated free flap monitoring system using artificial intelligence (AI), minimizing human involvement while maintaining efficiency.
UNASSIGNED: In this prognostic study, the designed system involves a smartphone camera installed in a location with optimal flap visibility to capture photographs at regular intervals. The automated program identifies the flap area, checks for notable abnormalities in its appearance, and notifies medical staff if abnormalities are detected. Implementation requires 2 AI-based models: a segmentation model for automatic flap recognition in photographs and a grading model for evaluating the perfusion status of the identified flap. To develop this system, flap photographs captured for monitoring were collected from patients who underwent free flap-based reconstruction from March 1, 2020, to August 31, 2023. After the 2 models were developed, they were integrated to construct the system, which was applied in a clinical setting in November 2023.
UNASSIGNED: Conducting the developed automated AI-based flap monitoring system.
UNASSIGNED: Accuracy of the developed models and feasibility of clinical application of the system.
UNASSIGNED: Photographs were obtained from 305 patients (median age, 62 years [range, 8-86 years]; 178 [58.4%] were male). Based on 2068 photographs, the FS-net program (a customized model) was developed for flap segmentation, demonstrating a mean (SD) Dice similarity coefficient of 0.970 (0.001) with 5-fold cross-validation. For the flap grading system, 11 112 photographs from the 305 patients were used, encompassing 10 115 photographs with normal features and 997 with abnormal features. Tested on 5506 photographs, the DenseNet121 model demonstrated the highest performance with an area under the receiver operating characteristic curve of 0.960 (95% CI, 0.951-0.969). The sensitivity for detecting venous insufficiency was 97.5% and for arterial insufficiency was 92.8%. When applied to 10 patients, the system successfully conducted 143 automated monitoring sessions without significant issues.
UNASSIGNED: The findings of this study suggest that a novel automated system may enable efficient flap monitoring with minimal use of clinician time. It may be anticipated to serve as an effective surveillance tool for postoperative free flap monitoring. Further studies are required to verify its reliability.
UNASSIGNED: To develop an automated free flap monitoring system using artificial intelligence (AI), minimizing human involvement while maintaining efficiency.
UNASSIGNED: In this prognostic study, the designed system involves a smartphone camera installed in a location with optimal flap visibility to capture photographs at regular intervals. The automated program identifies the flap area, checks for notable abnormalities in its appearance, and notifies medical staff if abnormalities are detected. Implementation requires 2 AI-based models: a segmentation model for automatic flap recognition in photographs and a grading model for evaluating the perfusion status of the identified flap. To develop this system, flap photographs captured for monitoring were collected from patients who underwent free flap-based reconstruction from March 1, 2020, to August 31, 2023. After the 2 models were developed, they were integrated to construct the system, which was applied in a clinical setting in November 2023.
UNASSIGNED: Conducting the developed automated AI-based flap monitoring system.
UNASSIGNED: Accuracy of the developed models and feasibility of clinical application of the system.
UNASSIGNED: Photographs were obtained from 305 patients (median age, 62 years [range, 8-86 years]; 178 [58.4%] were male). Based on 2068 photographs, the FS-net program (a customized model) was developed for flap segmentation, demonstrating a mean (SD) Dice similarity coefficient of 0.970 (0.001) with 5-fold cross-validation. For the flap grading system, 11 112 photographs from the 305 patients were used, encompassing 10 115 photographs with normal features and 997 with abnormal features. Tested on 5506 photographs, the DenseNet121 model demonstrated the highest performance with an area under the receiver operating characteristic curve of 0.960 (95% CI, 0.951-0.969). The sensitivity for detecting venous insufficiency was 97.5% and for arterial insufficiency was 92.8%. When applied to 10 patients, the system successfully conducted 143 automated monitoring sessions without significant issues.
UNASSIGNED: The findings of this study suggest that a novel automated system may enable efficient flap monitoring with minimal use of clinician time. It may be anticipated to serve as an effective surveillance tool for postoperative free flap monitoring. Further studies are required to verify its reliability.
摘要：
■精心的术后皮瓣监测对于防止皮瓣失败和在游离皮瓣手术中达到最佳效果至关重要，体检仍然是标准。尽管体检的可靠性很高，过度使用临床医生时间的要求被认为是主要缺点。
■要开发使用人工智能（AI）的自动自由皮瓣监测系统，尽量减少人的参与，同时保持效率。
■在这项预后研究中，设计的系统包括安装在具有最佳襟翼可见性的位置的智能手机摄像头，以定期拍摄照片。自动程序识别襟翼区域,检查其外观是否有明显的异常，如果发现异常，通知医务人员。实施需要2种基于AI的模型：用于照片中自动皮瓣识别的分割模型和用于评估已识别皮瓣的灌注状态的分级模型。为了开发这个系统，用于监测的皮瓣照片收集自2020年3月1日至2023年8月31日接受游离皮瓣重建的患者.在开发了这两种模型之后，他们被整合来构建系统，2023年11月在临床上应用。
■执行开发的基于AI的自动襟翼监测系统。
■开发模型的准确性和系统临床应用的可行性。
■照片来自305名患者（中位年龄，62年[范围，8-86岁]；男性为178[58.4%]）。根据2068张照片,FS-net程序(定制模型)是为皮瓣分割开发的，证明平均（SD）骰子相似系数为0.970（0.001），具有5倍交叉验证。对于襟翼分级系统,使用了305名患者的11112张照片，包括10115张具有正常特征的照片和997张具有异常特征的照片。在5506张照片上测试，DenseNet121模型表现出最高的性能，接收器工作特征曲线下面积为0.960（95％CI，0.951-0.969）。检测静脉功能不全的灵敏度为97.5％，动脉功能不全的灵敏度为92.8％。当应用于10名患者时，该系统成功进行了143次自动监控会话，没有出现重大问题。
■这项研究的结果表明，一种新颖的自动化系统可以在最少使用临床医生时间的情况下实现有效的皮瓣监测。可以预期作为术后游离皮瓣监测的有效监测工具。需要进一步的研究来验证其可靠性。
■要开发使用人工智能（AI）的自动自由皮瓣监测系统，尽量减少人的参与，同时保持效率。
■在这项预后研究中，设计的系统包括安装在具有最佳襟翼可见性的位置的智能手机摄像头，以定期拍摄照片。自动程序识别襟翼区域,检查其外观是否有明显的异常，如果发现异常，通知医务人员。实施需要2种基于AI的模型：用于照片中自动皮瓣识别的分割模型和用于评估已识别皮瓣的灌注状态的分级模型。为了开发这个系统，用于监测的皮瓣照片收集自2020年3月1日至2023年8月31日接受游离皮瓣重建的患者.在开发了这两种模型之后，他们被整合来构建系统，2023年11月在临床上应用。
■执行开发的基于AI的自动襟翼监测系统。
■开发模型的准确性和系统临床应用的可行性。
■照片来自305名患者（中位年龄，62年[范围，8-86岁]；男性为178[58.4%]）。根据2068张照片,FS-net程序(定制模型)是为皮瓣分割开发的，证明平均（SD）骰子相似系数为0.970（0.001），具有5倍交叉验证。对于襟翼分级系统,使用了305名患者的11112张照片，包括10115张具有正常特征的照片和997张具有异常特征的照片。在5506张照片上测试，DenseNet121模型表现出最高的性能，接收器工作特征曲线下面积为0.960（95％CI，0.951-0.969）。检测静脉功能不全的灵敏度为97.5％，动脉功能不全的灵敏度为92.8％。当应用于10名患者时，该系统成功进行了143次自动监控会话，没有出现重大问题。
■这项研究的结果表明，一种新颖的自动化系统可以在最少使用临床医生时间的情况下实现有效的皮瓣监测。可以预期作为术后游离皮瓣监测的有效监测工具。需要进一步的研究来验证其可靠性。
