Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.

In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.

Cisplatin-induced early-onset ototoxicity is linked to hearing loss. The mechanism by which cisplatin causes ototoxicity remains unclear. The purpose of this study was to identify the involvement of receptor-interacting protein kinase (RIP)3-dependent necroptosis in cisplatin-induced ototoxicity in vitro and in vivo. Sprague–Dawley rats (SD, 8 week) were treated via intraperitoneal (i.p.) injection with cisplatin (16 mg/kg for 1 day), and their hearing thresholds were measured by the auditory brainstem response (ABR) method. Hematoxylin and eosin (H & E) staining, immunohistochemistry, and western blots were performed to determine the effect of cisplatin-induced ototoxicity on cochlear morphology. Inhibitor experiments with necrostatin 1 (Nec-1) and Z-VAD were also performed in HEI-OC1 cell line. H&E stains revealed that the necroptotic changes were increased in the organ of Corti (OC) and spiral ganglion neurons (SGNs). Moreover, immunohistochemistry and western blot analysis showed that cisplatin treatment increased the protein levels of RIP3 in both OCs and SGNs. The treatment of Nec-1, a selective RIP1 inhibitor, resulted in markedly suppression of cisplatin-induced cell death in HEI-OC1 cells, whereas Z-VAD treatment did not change the cisplatin-induced cell death. Our results suggest that RIP3-dependent necroptosis was substantial in cisplatin-induced ototoxicity; inner cochlear regions, the OCs, and SGNs were especially sensitive to necroptosis.

A domain-specific multimodal generative AI model demonstrated potential for high diagnostic accuracy and clinical value in providing preliminary interpretations of chest radiographs for radiologists.

This study explores the internal standards for hearing tests and benefits of implementing international standard protocols, including the International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC), and discusses how ISO and IEC standards provide a framework for designing, calibrating, assessing hearing test instruments and methods, and exchanging and comparing data globally. ISO and IEC standards for hearing tests improve accuracy, reliability, and consistency of test results by applying standardized methods and environments. Moreover, they promote international harmonization and data interoperability, enabling information exchange and research collaboration. Those standards for hearing tests are beneficial but have challenges and limitations, such as variation in equipment and calibration, lag in updating standards, variation in implementation and compliance, and lack of coverage of clinical aspects, cultural diversity, and linguistic diversity. These affect the quality and interpretation of test results. Adapting ISO or IEC standards locally would improve their applicability and acceptability, while balancing customization and compatibility with global standards.