Dementia is a degenerative disease that is increasingly prevalent in an aging society. Alzheimer’s disease (AD), the most common type of dementia, is best mitigated via early detection and management. Deep learning is an artificial intelligence technique that has been used to diagnose and predict diseases by extracting meaningful features from medical images. The convolutional neural network (CNN) is a representative application of deep learning, serving as a powerful tool for the diagnosis of AD. Recently, vision transformers (ViT) have yielded classification performance exceeding that of CNN in some diagnostic image classifications. Because the brain is a very complex network with interrelated regions, ViT, which captures direct relationships between images, may be more effective for brain image analysis than CNN. Therefore, we propose a method for classifying dementia images by applying 18F-Florbetaben positron emission tomography (PET) images to ViT. Data were evaluated via binary (normal control and abnormal) and ternary (healthy control, mild cognitive impairment, and AD) classification. In a performance comparison with the CNN, VGG19 was selected as the comparison model. Consequently, ViT yielded more effective performance than VGG19 in binary classification. However, in ternary classification, the performance of ViT cannot be considered excellent. These results show that it is hard to argue that the ViT model is better at AD classification than the CNN model.

Current research on medical image processing relies heavily on the amount and quality of input data. Specifically, supervised machine learning methods require well-annotated datasets. A lack of annotation tools limits the potential to achieve high-volume processing and scaled systems with a proper reward mechanism. We developed MarkIt, a web-based tool, for collaborative annotation of medical imaging data with artificial intelligence and blockchain technologies. Our platform handles both Digital Imaging and Communications in Medicine (DICOM) and non-DICOM images, and allows users to annotate them for classification and object detection tasks in an efficient manner. MarkIt can accelerate the annotation process and keep track of user activities to calculate a fair reward. A proof-of-concept experiment was conducted with three fellowship-trained radiologists, each of whom annotated 1,000 chest X-ray studies for multi-label classification. We calculated the inter-rater agreement and estimated the value of the dataset to distribute the reward for annotators using a crypto currency. We hypothesize that MarkIt allows the typically arduous annotation task to become more efficient. In addition, MarkIt can serve as a platform to evaluate the value of data and trade the annotation results in a more scalable manner in the future. The platform is publicly available for testing on https://markit.mgh.harvard.edu.

With more than 300,000 patients per year in the United States alone, hip fractures are one of the most common injuries occurring in the elderly. The incidence is predicted to rise to 6 million cases per annum worldwide by 2050. Many fracture registries have been established, serving as tools for quality surveillance and evaluating patient outcomes. Most registries are based on billing and procedural codes, prone to under-reporting of cases. Deep learning (DL) is able to interpret radiographic images and assist in fracture detection; we propose to conduct a DL-based approach intended to autocreate a fracture registry, specifically for the hip fracture population. Conventional radiographs (n = 18,834) from 2919 patients from Massachusetts General Brigham hospitals were extracted (images designated as hip radiographs within the medical record). We designed a cascade model consisting of 3 submodules for image view classification (MI), postoperative implant detection (MII), and proximal femoral fracture detection (MIII), including data augmentation and scaling, and convolutional neural networks for model development. An ensemble model of 10 models (based on ResNet, VGG, DenseNet, and EfficientNet architectures) was created to detect the presence of a fracture. The accuracy of the developed submodules reached 92%-100%; visual explanations of model predictions were generated through gradient-based methods. Time for the automated model-based fracture-labeling was 0.03 seconds/image, compared with an average of 12 seconds/image for human annotation as calculated in our preprocessing stages. This semisupervised DL approach labeled hip fractures with high accuracy. This mitigates the burden of annotations in a large data set, which is time-consuming and prone to under-reporting. The DL approach may prove beneficial for future efforts to autocreate construct registries that outperform current diagnosis and procedural codes. Clinicians and researchers can use the developed DL approach for quality improvement, diagnostic and prognostic research purposes, and building clinical decision support tools.

Myong Y, et al. Korean J Radiol. 2026 ;27:e101. https://doi.org/10.3348/kjr.2026.1648

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