Deep learning for hydrophone big data

PurposeTo differentiate complicated appendicitis (CA) from uncomplicated appendicitis (UA) in children, we developed and validated an artificial intelligence (AI) model using a multimodal approach integrating ultrasound images and clinical data.MethodsA retrospective analysis was performed on 372 pathologically confirmed pediatric appendicitis cases (230 male, 142 female) from three centers, all with preoperative abdominal ultrasound. Deep learning (DL) features and radiomic features were extracted from appendiceal ultrasound images using deep transfer learning (DTL) and conventional radiomic methods, respectively. We selected 12 radiomic features, 9 DL features, and 3 clinical features—namely, white blood cell count (WBC), neutrophil count (NEU), and C-reactive protein (CRP)—for building the machine learning classification model. Based on these features, four distinct models were constructed: the Rad model (radiomic features only), the DL model (DL features only), the DTL model (combined radiomic and DL features), and the Combine model (integrating all three feature types: radiomic, DL, and clinical features). Model performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the DeLong test. Finally, the combined model was compared to the performance of clinicians with varying levels of experience.ResultsThe combined model demonstrated consistently favorable performance across all cohorts (AUC: 0.940, 0.895, 0.866, and 0.783 for training and validation sets, respectively). The model's accuracy (0.862) and positive predictive value (0.896) were comparable to senior surgeons (0.741, 0.970) and superior to junior surgeons (0.672, 0.865) in the internal validation cohort. DCA confirmed the clinical utility of the combined model over conventional strategies.ConclusionOur ultrasound-based AI model provides reliable differentiation between CA and UA in children, offering potential value as a diagnostic support tool for clinical decision making.

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Skeleton-based deep pose feature learning for action quality assessment on figure skating videos

ObjectiveThis study aims to create a reliable framework for grading esophageal cancer. The framework combines feature extraction, deep learning with attention mechanisms, and radiomics to ensure accuracy, interpretability, and practical use in tumor analysis.Materials and methodsThis retrospective study used data from 2,560 esophageal cancer patients across multiple clinical centers, collected from 2018 to 2023. The dataset included CT scan images and clinical information, representing a variety of cancer grades and types. Standardized CT imaging protocols were followed, and experienced radiologists manually segmented the tumor regions. Only high-quality data were used in the study. A total of 215 radiomic features were extracted using the SERA platform. The study used two deep learning models—DenseNet121 and EfficientNet-B0—enhanced with attention mechanisms to improve accuracy. A combined classification approach used both radiomic and deep learning features, and machine learning models like Random Forest, XGBoost, and CatBoost were applied. These models were validated with strict training and testing procedures to ensure effective cancer grading.ResultsThis study analyzed the reliability and performance of radiomic and deep learning features for grading esophageal cancer. Radiomic features were classified into four reliability levels based on their ICC (Intraclass Correlation) values. Most of the features had excellent (ICC > 0.90) or good (0.75 < ICC ≤ 0.90) reliability. Deep learning features extracted from DenseNet121 and EfficientNet-B0 were also categorized, and some of them showed poor reliability. The machine learning models, including XGBoost and CatBoost, were tested for their ability to grade cancer. XGBoost with Recursive Feature Elimination (RFE) gave the best results for radiomic features, with an AUC (Area Under the Curve) of 91.36%. For deep learning features, XGBoost with Principal Component Analysis (PCA) gave the best results using DenseNet121, while CatBoost with RFE performed best with EfficientNet-B0, achieving an AUC of 94.20%. Combining radiomic and deep features led to significant improvements, with XGBoost achieving the highest AUC of 96.70%, accuracy of 96.71%, and sensitivity of 95.44%. The combination of both DenseNet121 and EfficientNet-B0 models in ensemble models achieved the best overall performance, with an AUC of 95.14% and accuracy of 94.88%.ConclusionsThis study improves esophageal cancer grading by combining radiomics and deep learning. It enhances diagnostic accuracy, reproducibility, and interpretability, while also helping in personalized treatment planning through better tumor characterization.Clinical trial numberNot applicable.

The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathologically confirmed placenta invasion and 40 pregnant women with normal placenta. After 24 gestational weeks, all participants underwent magnetic resonance imaging. The uterus and placenta regions were segmented in magnetic resonance images on sagittal T2WI. Ninety-three radiomics features were extracted from the placenta region, and 128 deep features were extracted from the uterus region using a deep neural network. The least absolute shrinkage and selection operator (LASSO) algorithm was used to filter these 221 features and to form the combined signature. Then the combined signature (CS) and clinical factors were combined to construct a nomogram. The accuracy, sensitivity, specificity and AUC of the nomogram were compared with four machine learning methods. The model NDRC was trained on the dataset of 78 pregnant women in the training cohort. Finally, the model NDRC was compared with four machine learning methods on the independent validation cohort of 34 pregnant women. The results showed that the prediction accuracy, sensitivity, specificity and AUC of the NDRC model were 0.941, 0.952, 0.923 and 0.985 respectively, which outperforms the traditional machine learning methods which rely on radiomics features and deep learning features alone.

Along with the popularity of intelligent services and mobile services, service recommendation has become a key task, especially the task based on quality-of-service (QoS) in edge computing environment. Most existing service recommendation methods have some serious defects, and cannot be directly adopted in edge computing environment. For example, most of existing methods cannot learn deep features of users or services, but in edge computing environment, there are a variety of devices with different configurations and different functions, and it is necessary to learn deep features behind those complex devices. In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN). The proposed mode is named Joint CNN-MF (JCM). JCM is capable of using the learned deep latent features of neighbors to infer the features of a user or a service. Meanwhile, to improve the accuracy of neighbors selection, the proposed model contains a novel similarity computation method. CNN learns the neighbors features, forms a feature matrix and infers the features of the target user or target service. We conducted experiments on a real-world service dataset under a batch of cases of data densities, to reflect the complex invocation cases in edge computing environment. The experimental results verify that compared to counterpart methods, our method can consistently achieve higher QoS prediction results.

An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning

The deep convolutional neural network has been extensively applied for clinical computer-aided diagnosis. In this study, we combine deep learning feature extraction and eXtreme gradient boosting (XGBoost) classifier for predicting the risk of esophageal varices (EV). First, the quantitative deep learning features and radiomics features of the regions of interest which includes spleen, liver and esophageal are extracted and concatenated. Then, XGBoost and the Least Absolute Shrinkage and Selection Operator (LASSO) are applied for the optimal predictive features selection and prediction of EV risk. XGBoost is used to assess the significance of the extracted features and LASSO is used to select the distinctive features. Finally, random forest, XGBoost and support vector machine classification methods are applied for predicting the low-risk and high-risk of esophageal varices. We collected computed tomography images of cirrhotic patients in two hospitals as the independent training and validation sets. Experimental results show that the features of esophageal are more distinctive than that of other organs. Moreover, the combination of deep learning and radiomics features based on XGBoost algorithm has outperforming classification performance in predicting the severity of EV disease compared to existing approaches.KeywordsEsophageal varicesDeep learningRadiomicsXGBoost

The objective of this study is to investigate the value of radiomics features and deep learning features based on positron emission tomography/computed tomography (PET/CT) in predicting perineural invasion (PNI) in rectal cancer. We retrospectively collected 120 rectal cancer (56 PNI-positive patients 64 PNI-negative patients) patients with preoperative 18F-FDG PET/CT examination and randomly divided them into training and validation sets at a 7:3 ratio. We also collected 31 rectal cancer patients from two other hospitals as an independent external validation set. χ2 test and binary logistic regression were used to analyze PET metabolic parameters. PET/CT images were utilized to extract radiomics features and deep learning features. The Mann-Whitney U test and LASSO were employed to select valuable features. Metabolic parameter, radiomics, deep learning and combined models were constructed. ROC curves were generated to evaluate the performance of models. The results indicate that metabolic tumor volume (MTV) is correlated with PNI (P = 0.001). In the training set and validation set, the AUC values of the metabolic parameter model were 0.673 (95%CI: 0.572-0.773), 0.748 (95%CI: 0.599-0.896). We selected 16 radiomics features and 17 deep learning features as valuable factors for predicting PNI. The AUC values of radiomics model and deep learning model were 0.768 (95%CI: 0.667-0.868) and 0.860 (95%CI: 0.780-0.940) in the training set. And the AUC values in the validation set were 0.803 (95%CI: 0.656-0.950) and 0.854 (95% CI 0.721-0.987). Finally, the combined model exhibited AUCs of 0.893 (95%CI: 0.825-0.961) in the training set and 0.883 (95%CI: 0.775-0.990) in the validation set. In the external validation set, the combined model achieved an AUC of 0.829 (95% CI: 0.674-0.984), outperforming each individual model. The decision curve analysis of these models indicated that using the combined model to guide treatment provided a substantial net benefit. This combined model established by integrating PET metabolic parameters, radiomics features, and deep learning features can accurately predict the PNI in rectal cancer.

Basketball shooting angle calculation and analysis by deeply-learned vision model

Rationale and ObjectivesAccurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep learning radiomics, feature fusion, and decision fusion models based on multi-region MR habitat imaging using six machine learning classifiers. Materials and MethodsWe retrospectively included 300 HCC patients. The intratumoral and peritumoral regions were segmented into distinct habitats, from which radiomics and deep learning features were extracted using arterial phase MR images. To reduce feature dimensionality, we applied intra-class correlation coefficient (ICC) analysis, Pearson correlation coefficient (PCC) filtering, and recursive feature elimination (RFE). Based on the selected optimal features, prediction models were constructed using decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost (XGB) classifiers. Additionally, fusion models were developed utilizing both feature fusion and decision fusion strategies. The performance of these models was validated using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. ResultsThe decision fusion model (VOI-Peri10-1) using LR and integrating clinicoradiologic, radiomics, and deep learning features achieved the highest AUC of 0.808 (95% confidence interval [CI]: 0.807–0.912) in the test cohort, with good calibration (Hosmer-Lemeshow test, P > 0.050) and clinical net benefit. ConclusionThe LR-based decision fusion model integrating clinicoradiologic, radiomics, and deep learning features shows promise for preoperative prediction of MVI in HCC, aiding in patient outcome predictions and personalized treatment planning.

The complementarity of a diverse range of deep learning features extracted from video content for video recommendation

BackgroundIdentifying predictive markers for immunotherapy in non-small cell lung cancer (NSCLC) is critical for personalized treatment. This study aimed to construct a predictive model that integrates clinical features, enhanced computed tomography (CT)-radiomics, and deep learning (DL) features for the assessment of durable clinical benefit (DCB) from immunotherapy in patients with advanced NSCLC and to provide biological interpretability to predictions by integrating radiogenomic data.MethodsWe conducted a retrospective analysis of 201 advanced NSCLC patients who underwent immunotherapy with CT images, with data supplemented from The Cancer Imaging Archive (TCIA). Radiomics features (RFs) were extracted from enhanced CT images, and DL features were derived using a pre-trained ResNet-34 model. DCB-related signatures were constructed using the least absolute shrinkage and selection operator (LASSO) algorithm, and fusion nomogram models were developed by integrating significant clinical variables, radiomics, and DL features. Shapley additive explanations were employed to quantify the impact of radiomics-DL features on model predictions. Gene set enrichment and biological correlation analyses based on transcriptomic TCIA data were performed to explore the biological significance of radiomics-DL score.ResultsStatistically significant clinical predictors included initial efficacy, brain metastases, programmed death-ligand 1 (PD-L1) expression, and hemoglobin levels. The fusion nomogram model demonstrated the highest predictive accuracy for DCB, with area under the curve (AUC) values of 0.843 in the train cohort and 0.894 in the test cohort, surpassing individual feature sets. Biological exploration revealed associations between radiomics-DL score and biological characteristics, including immune responses and immunoregulation.ConclusionsThis integrated approach shows the potential of combining clinical, radiomics and deep learning features (DLFs) as a noninvasive biomarker for predicting immunotherapy efficacy in NSCLC, assisting in patient selection and clinical decision-making. Radiotranscriptomic analysis may reveal key cellular and immune patterns associated with radiomics-DL signature.

The inertial measurement unit (IMU) is a popular sensor device, which is mainly employed to acquire body or hand gesture action information for performing specific recognition tasks. We present a dual-channel artificial neural network (ANN) recognition decision hybridization scheme incorporated with deep leaning of IMU-based spectrogram images for cognition of several common hand gesture intention categorization actions focused on the 6-axis IMU sensing data (containing 3-axis accelerometer and 3-axis gyroscope information) and the 6-axis IMU derived spectrogram images. In this hand gesture intention cognition approach, both symmetric and asymmetric ANN structures are considered for intention action classifications. The proposed dual-channel ANN decision fusion framework contains one ANN recognition channel with inputs of &#x201C;6-axis IMU raw data&#x201D; and the other ANN recognition channel with inputs of &#x201C;IMU spectrogram image derived-critical deep learning features&#x201D;. Recognition decisions estimated from either of these two ANN recognition channels form the fusion framework. Three fusion schemes on dual-channel ANN recognition decisions are presented in this study, channel output layer accumulation, same channel candidate output and same-or-dual channel candidate output. In this study, the well-known deep learning neural network, visual&#xA0;geometry group- convolution neural network (VGG-CNN), is employed to carry out deep learning computations on IMU-based spectrogram images, from which, the critical deep learning feature of each spectrogram image can then be extracted and used as an input for the dual-channel ANN. For recognition performance comparisons, hand gesture intention recognition by the traditional VGG-CNN deep neural network approach (i.e. recognition of IMU spectrogram images using typical deep learning of the CNN model) is also performed. Experiments on classifications of six hand gesture intention actions show that the presented dual-channel ANN decision fusion incorporated with deep learning of IMU spectrum images has competitive performances, reaching better recognition accuracy than traditional CNN deep learning.

Identifying final pathology of FNA-indeterminant nodules before surgical resection could decrease the number of unnecessary surgeries and total cost to patients. This project explores how radiomics (RM) and deep learning (DTLM) models may be combined to improve the potential for clinical interpretability of machine learning models in the task of classifying indeterminant thyroid nodules on ultrasound. Two radiomic and deep learning combination models were created: a simple classifier combination model (SCM) and an interpretability-driven combination model (ICM). SCM provided a nodule malignancy score. ICM merged radiomic and deep learning features through correlation and provided echogenicity-related, composition-related, and shape/margin-related malignancy scores which were averaged to yield an overall nodule malignancy score. Models were trained and tested on a de-identified dataset of 476 grayscale ultrasound images collected under IRB approval containing 222 images from 69 indeterminant nodules with a final pathology of malignant and 254 images from 82 indeterminant nodules with a final pathology of benign. Models were tested using 5- fold cross-validation by nodule over 100 iterations. Receiver-operating characteristic (ROC) analysis was conducted with area under the ROC curve (AUC) serving as the statistic of merit for model performance. Models yielded mean AUC [95%CI] of 0.75 [.67,.83], 0.70 [.62,.78], 0.77 [0.70,0.84], 0.76 [.69,.84] for RM, DTLM, SCM, and ICM respectively. This work failed to demonstrate a statistically significant difference in model performances. However, the ICM presents a novel method for combining radiomics and deep learning features focused on improving interpretability for clinical implementation in the task of indeterminant thyroid nodule classification.