Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images.

A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires specialized knowledge. In addition, inappropriate annotation processes can increase model bias. Self-supervised learning (SSL) is a type of unsupervised learning method that extracts image representations. Thus, SSL can be an effective method to reduce the number of labeled images. In this study, we investigated the feasibility of reducing the number of labeled images in a limited set of unlabeled medical images. The unlabeled chest X-ray (CXR) images were pretrained using the SimCLR framework, and then the representations were fine-tuned as supervised learning for the target task. A total of 2000 task-specific CXR images were used to perform binary classification of coronavirus disease 2019 (COVID-19) and normal cases. The results demonstrate that the performance of pretraining on task-specific unlabeled CXR images can be maintained when the number of labeled CXR images is reduced by approximately 40%. In addition, the performance was significantly better than that obtained without pretraining. In contrast, a large number of pretrained unlabeled images are required to maintain performance regardless of task specificity among a small number of labeled CXR images. In summary, to reduce the number of labeled images using SimCLR, we must consider both the number of images and the task-specific characteristics of the target images.

Performance change with the number of training data: A case study on the binary classification of COVID-19 chest X-ray by using convolutional neural networks.

One of the features of artificial intelligence/machine learning-based medical devices resides in their ability to learn from real-world data. However, obtaining a large number of training data in the early phase is difficult, and the device performance may change after their first introduction into the market. To introduce the safety and effectiveness of these devices into the market in a timely manner, an appropriate post-market performance change plan must be established at the timing of the premarket approval. In this work, we evaluate the performance change with the variation of the number of training data. Two publicly available datasets are used: one consisting of 4000 images for COVID-19 and another comprising 4000 images for Normal. The dataset was split into 7000 images for training and validation, also 1000 images for test. Furthermore, the training and validation data were selected as different 16 datasets. Two different convolutional neural networks, namely AlexNet and ResNet34, with and without a fine-tuning method were used to classify two image types. The area under the curve, sensitivity, and specificity were evaluated for each dataset. Our result shows that all performances were rapidly improved as the number of training data was increased and reached an equilibrium state. AlexNet outperformed ResNet34 when the number of images was small. The difference tended to decrease as the number of training data increased, and the fine-tuning method improved all performances. In conclusion, the appropriate model and method should be selected considering the intended performance and available number of data.

Regulatory convergence of medical devices: a case study using ISO and IEC standards.

INTRODUCTION: Achieving regulatory convergence is important in providing safe and effective medical devices to patients in a timely manner. The use of standards set by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), may be an important tool for regulatory convergence. The International Medical Device Regulators Forum (IMDRF) published a survey and statements regarding the use of these standards in each IMDRF jurisdiction, which showed that each jurisdiction proactively uses these standards in its regulation. AREAS COVERED: This review describes the current situation by comparing the ISO and IEC standards with regulations in the European Union, the USA, and Japan on the basis of third-party certification in Japan. EXPERT COMMENTARY: Our results show that ISO and IEC standards may be important tools for regulatory convergence. However, the corresponding Technical Committees and publication editions vary in each regulation. Furthermore, there are cases in which inconsistencies between the requirements of these standards and regulation may arise. Considering this background, it is important that jurisdictions have common consensus about which Technical Committees are appropriate for the regulation of medical devices and the importance of involving standards development at an early stage to reflect regulatory opinions.