Bandari Ela, Beuzen Tomas, Habashy Lara, Raza Javairia, Yang Xudong, Kapeluto Jordanna, Meneilly Graydon, Madden Kenneth
Data Science Program, University of British Columbia, Vancouver, BC, Canada.
Gerontology and Diabetes Research Laboratory, University of British Columbia, Vancouver, BC, Canada.
JMIR Form Res. 2022 May 6;6(5):e34830. doi: 10.2196/34830.
The most common dermatological complication of insulin therapy is lipohypertrophy.
As a proof of concept, we built and tested an automated model using a convolutional neural network (CNN) to detect the presence of lipohypertrophy in ultrasound images.
Ultrasound images were obtained in a blinded fashion using a portable GE LOGIQ e machine with an L8-18I-D probe (5-18 MHz; GE Healthcare). The data were split into train, validation, and test splits of 70%, 15%, and 15%, respectively. Given the small size of the data set, image augmentation techniques were used to expand the size of the training set and improve the model's generalizability. To compare the performance of the different architectures, the team considered the accuracy and recall of the models when tested on our test set.
The DenseNet CNN architecture was found to have the highest accuracy (76%) and recall (76%) in detecting lipohypertrophy in ultrasound images compared to other CNN architectures. Additional work showed that the YOLOv5m object detection model could be used to help detect the approximate location of lipohypertrophy in ultrasound images identified as containing lipohypertrophy by the DenseNet CNN.
We were able to demonstrate the ability of machine learning approaches to automate the process of detecting and locating lipohypertrophy.
胰岛素治疗最常见的皮肤并发症是脂肪增生。
作为概念验证，我们构建并测试了一个使用卷积神经网络（CNN）的自动化模型，以检测超声图像中脂肪增生的存在。
使用配备L8 - 18I - D探头（5 - 18 MHz；通用电气医疗集团）的便携式通用电气LOGIQ e机器以盲法获取超声图像。数据分别分为训练集、验证集和测试集，比例分别为70%、15%和15%。鉴于数据集规模较小，采用图像增强技术来扩大训练集规模并提高模型的泛化能力。为比较不同架构的性能，团队在测试集上测试模型时考虑了模型的准确率和召回率。
与其他CNN架构相比，发现DenseNet CNN架构在检测超声图像中的脂肪增生方面具有最高的准确率（76%）和召回率（76%）。进一步的工作表明，YOLOv5m目标检测模型可用于帮助检测被DenseNet CNN识别为包含脂肪增生的超声图像中脂肪增生的大致位置。
我们能够证明机器学习方法具有自动化检测和定位脂肪增生过程的能力。
