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• SwinUNETR + uncertainty quantification aids acute ischemic stroke diagnosis via NCCT. • The proposed model predicted lesion volume with a mean Dice score: 46.7 %. • Our method was tested on the public AISD dataset, resulting in a Dice score: 61.9 %. • The study explores feasibility of the model's applications in the clinical scenario. We adopt the existing Deep learning architecture to support diagnosing acute ischemic stroke by automatically detecting lesion location on 3D non-contrast CT brain scans. We also investigate the feasibility of the model's applications in the clinical scenario by data analysis. We retrospectively collected 3D non-contrast CT scans of 317 patients with acute ischemic stroke from the China Medical University Hospital. All patients underwent standard baseline non-contrast CT scanning followed by diffusion-weighted imaging. We utilized these data for training the existing model – SwinUNETR, which includes a self-attention module as an encoder and a convolutional-based decoder. Moreover, the software innovatively incorporates uncertainty quantification to enhance model performance. In the test set, the AI model predicted lesion volume with a mean Dice score of 46.7 % compared to diffusion-weighted imaging verified by experts. The model completed the analysis on a 3D non-contrast CT scan in approximately 30 s. The average difference between the model-segmented acute ischemic stroke lesion volume (67.11 ml) and diffusion-weighted imaging lesion volume (35.2 ml) was 27.09 ml. Pearson correlation of lesion volume between prediction and ground truth is 83.46 %. We also found our model has superior performance in the CT scan with lesion volume > 40 ml and 3 h < onset-to-CT time <24 h. Moreover, our approach was applied to the AISD public dataset, yielding a Dice score of 0.619 upon testing. This model could help facilitate timely and accurate diagnosis of acute ischemic stroke in a clinical emergency setting and low-resourced hospital. [ABSTRACT FROM AUTHOR]

• This article aimed to utilize magnetic resonance images and arthroscopic findings as a dataset to develop an artificial intelligence model that can help detect meniscal lesions, and thus aid in the diagnosis of meniscus injuries. • Our dataset comprised data from 811 knee magnetic resonance imaging studies. magnetic resonance images were labeled and annotated by two orthopedic surgeons. • First, Scaled-YOLOv4 was used to detect the position of the meniscus. Second, the EfficientNet-B7 model structure was used to detect meniscal tears. • Through the artificial intelligence detection system, clinicians can obtain a structured report of meniscus rupture, which enables physicians to interpret images more quickly and saves time for physicians to read images. Magnetic resonance imaging (MRI) examinations are often necessary for the diagnosis of meniscal injuries. Due to the quantity and detail of the images in an MRI examination, a careful reading of MR images is time-consuming even for experienced physicians. The use of artificial intelligence (AI) models for interpreting MR images may address this problem. This study aimed to utilize MR images and arthroscopic findings as a dataset to develop an AI model that can help detect meniscal lesions, and thus aid in the diagnosis of meniscus injuries. Our dataset comprised data from 811 knee MRI studies. MR images were labeled and annotated by two orthopedic surgeons. The training pipeline had two parts. First, Scaled-YOLOv4 was used to detect the position of the meniscus. Second, the EfficientNet-B7 model structure was used to detect meniscal tears. A brief report demonstrating the probability and the position of a normal or torn meniscus was then generated by the AI-based clinical decision support system. The Scaled-YOLOv4 model had areas under the curve (AUCs) of 0.948 and 0.963 in the sagittal and coronal views, respectively. The EfficientNet-B7 model yielded AUCs of 0.984 and 0.972 in the sagittal and coronal views, respectively. High AUCs were achieved both in meniscus localization and meniscal tear detection. In this study, we utilized arthroscopic findings, obtained using the optimal meniscal tear diagnosis method, as our ground truth label. Nevertheless, this approach did not provide better model performance than that based on MRI clinical reports. The contributions of this paper include the following points. First, an AI-based clinical decision support system was established to detect meniscal injury based on a deep learning model algorithm. High sensitivity and specificity were achieved by the model system. The model may have substantially facilitated the diagnosis of meniscal injuries from MR images. Second, through the AI detection system, clinicians can obtain a structured report of meniscus rupture, which enables physicians to interpret images more quickly and saves time for physicians to read images. [ABSTRACT FROM AUTHOR]

Abstract: We herein demonstrate that the hybrid sensor PA1611 carries out specific signal transduction, through HptB (PA3345), to the response regulator PA3346 in Pseudomonas aeruginosa PAO1. As assessed by phenotypic changes in the hptB deletion mutant, the pathway is likely to be involved in the regulation of flagellar activity, the chemotaxis response, twitching motility, and biofilm formation in the bacteria. [Copyright &y& Elsevier]