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Purpose To explore a multidomain fusion model of radiomics and deep learning features based on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. Materials and methods This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37–90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32–88 years). Three different methods were discussed to identify PDAC and AIP based on 18 F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. Results The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4–97.3%), 90.1% (95% CI 88.7–91.5%), 87.5% (95% CI 84.3–90.6%), and 93.0% (95% CI 90.3–95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. Conclusion The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on 18 F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.

Nystagmus information is an essential basis for diagnosing benign paroxysmal positional vertigo (BPPV), and nystagmus recorders are a crucial way to obtain nystagmus information. We designed a new wireless video nystagmus recorder, which uses the OV4689 sensor to collect the nystagmus video, encodes it on the RK3399 core control board, and sends it to the host computer through the 5GHz WiFi module. Compared with the current nystagmus recorder, it has the advantages of low price, convenient operation, and high frame rate. This paper uses a novel semantic segmentation network based on the Yolov5 target detection network and the improved Deplabv3+ segmentation module to perform real-time pupil segmentation on the collected video. Based on the segmentation results, we use ellipse fitting to extract the boundary and center of the pupil for drawing the pupil activity trajectory. Compared with the existing pupil tracking method, the method divides the pupil area faster and with higher precision, ensuring the pupil center positioning accuracy. The technique can effectively assist doctors in the diagnosis of BPPV.