Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.

PURPOSE: Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation. METHODS: The super-resolution convolutional neural network (SRCNN) model was employed to enhance the resolution of a given dataset, followed by data augmentation using CycleGAN. Subsequently, the ViT model facilitated the detection and classification of urinary tract stones. The model's performance was evaluated using accuracy, precision, and recall as metrics. RESULTS: The deep learning model based on ViT showed superior performance compared to other existing models. Furthermore, the performance increased with the size of the backbone model. CONCLUSION: The study proposes a way to utilize medical data to improve the diagnosis of urinary tract stones. SRCNN was used for data preprocessing to enhance resolution, while CycleGAN was utilized for data augmentation. The ViT model was utilized for stone detection, and its performance was validated through metrics such as accuracy, sensitivity, specificity, and the F1 score. It is anticipated that this research will aid in the early diagnosis and treatment of urinary tract stones, thereby improving the efficiency of medical personnel.

Transfer Learning for Effective Urolithiasis Detection.

PURPOSE: Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology. METHODS: The ResNet50 model was employed to develop feature extractors for detecting urinary tract stones. Transfer learning was applied by utilizing the weights of pretrained models as initial values, and the models were fine-tuned with the provided data. The model's performance was evaluated using accuracy, precision-recall, and receiver operating characteristic curve metrics. RESULTS: The ResNet-50-based deep learning model demonstrated high accuracy and sensitivity, outperforming traditional methods. Specifically, it enabled a rapid diagnosis of the presence or absence of urinary tract stones, thereby assisting doctors in their decision-making process. CONCLUSION: This research makes a meaningful contribution by accelerating the clinical implementation of urinary tract stone detection technology utilizing ResNet-50. The deep learning model can swiftly identify the presence or absence of urinary tract stones, thereby enhancing the efficiency of medical staff. We expect that this study will contribute to the advancement of medical imaging diagnostic technology based on deep learning.

Optimal conditions for cryopreservation of primary chicken embryo kidney cells with dimethyl sulfoxide.

Conditions were evaluated for optimum cryopreservation of primary chicken embryo kidney (CEK) cells. The recovery of viable CEK cells was best (50.8% viability) when the concentration of dimethyl sulfoxide (DMSO) in the freezing medium was 20% (v/v). The viability of primary CEK cells was not influenced by the concentration of calf serum in the freezing medium, the duration of storage at -70 degrees C before storage in liquid nitrogen, cell concentration, or the method of addition or dilution of DMSO. Thawed cells recovered and grew in complete growth medium similarly to cells freshly isolated from kidney, and influenza viruses produced plaques in the monolayer. The cryopreservation procedures described here may facilitate maintenance of a standard stock of primary CEK cells for laboratories where preparation of primary CEK cells is not an option.

A multiplex RT-PCR method for screening of reassortant live influenza vaccine virus strains.

A system based on reverse transcription polymerase chain reaction (RT-PCR) of the RNA genome was established to identify genetic composition of influenza viruses generated by reassortment between an attenuated donor virus and virulent wild type virus. The primers were designed, by multiple sequence alignment of variable regions, specific for cold-adapted donor virus HTCA-A 101, as compared to other influenza A viruses. The specificity of each primer set was confirmed and the primers were combined to perform RT-PCR in multiplex manner. The multiplex PCR was adopted to distinguish the 6:2 reassortant viruses containing six internal genome segments of attenuated donor virus and two surface antigens of virulent strain from the wild type viruses. The method allowed us to optimize the reassorting process on a routine basis and to confirm the selection of reassortant clones efficiently. The method is suitable for analyzing the contribution of specific gene segments for growth and attenuating characteristics and for generation of live attenuated vaccine by annual reassortment.