We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data.

The clinical efficacy of robotic rehabilitation interventions hinges on appropriate neuromuscular recruitment from the patient. The first purpose of this study was to evaluate the use of supervised machine learning techniques to predict neuromuscular recruitment of the ankle plantar flexors during walking with ankle exoskeleton resistance in individuals with cerebral palsy (CP). The second goal of this study was to utilize the predictive models of plantar flexor recruitment in the design of a personalized biofeedback framework intended to improve (i.e., increase) user engagement when walking with resistance. First, we developed and trained multilayer perceptrons (MLPs), a type of artificial neural network (ANN), utilizing features extracted exclusively from the exoskeleton\'s onboard sensors, and demonstrated 85-87% accuracy, on average, in predicting muscle recruitment from electromyography measurements. Next, our participants completed a gait training session while receiving audio-visual biofeedback of their personalized real-time planar flexor recruitment predictions from the online MLP. We found that adding biofeedback to resistance elevated plantar flexor recruitment by 24 16% compared to resistance alone. This study highlights the potential for online machine learning frameworks to improve the effectiveness and delivery of robotic rehabilitation systems in clinical populations.

(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87-91), the specificity was 90% (95% CI: 87-92), and the AUC was 0.95 (95% CI: 0.93-0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.

The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

The production of electronic waste, also known as e-waste, has risen with the growing reliance on electronic products. To reduce negative environmental impact and achieve sustainable industrial processes, recovering and reusing products is crucial. Advances in AI and robotics can help in this effort by reducing workload for human workers and allowing them to stay away from hazardous materials. However, autonomous human motion/intention perception is a primary barrier in e-waste remanufacturing. To address the research gap, this study combined experimental data collection with deep learning models for accurate disassembly task recognition. Over 570,000 frames of motion data were collected from inertial measurement units (IMU) worn by 22 participants. A novel sequence-based correction (SBC) algorithm was also proposed to further improve the accuracy of the overall pipeline. Results showed that models (CNN, LSTM, and GoogLeNet) had an overall accuracy of 88-92%. The proposed SBC algorithm improved accuracy to 95%.

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Powered lower-limb prosthetic devices may be becoming a promising option for amputation patients. Although various methods have been proposed to produce gait trajectories similar to those of non-disabled individuals, implementing these control methods is still challenging. It remains unclear whether these methods provide appropriate, safe, and intuitive locomotion as intended. This paper proposes the direct mapping of the voluntary movement of a residual limb (i.e., thigh) to the desired impedance parameters for amputated limbs (i.e., knee and ankle). The proposed model was learned from the gait trajectories of intact limb individuals from a publicly available biomechanics dataset, and was applied to control the prosthetic leg without post-tuning the network. Thus, the proposed method does not require training time with individuals with amputation nor configuration time for its use, and it provides a closely resembling gait trajectory of the intact limb. For preliminary testing, three able-bodied subjects participated in bypass tests. The proposed model accomplished intuitive and reliable level-ground walking at three different step lengths: self-selected, long-, and short-step lengths. The results indicate that intact benchmark data with different sensor configurations can be directly used to train the model to control prosthetic legs.

UNASSIGNED: Prolonged air leak is the most common complication of thoracic surgery. Intraoperative leak site detection is the first step in decreasing the risk of leak-related postoperative complications.
UNASSIGNED: We retrospectively reviewed the surgical videos of patients who underwent lung resection at our institution. In the training phase, deep learning-based air leak detection software was developed using leak-positive endoscopic images. In the testing phase, a different data set was used to evaluate our proposed application for each predicted box.
UNASSIGNED: A total of 110 originally captured and labeled images obtained from 70 surgeries were preprocessed for the training data set. The testing data set contained 64 leak-positive and 45 leak-negative sites. The testing data set was obtained from 93 operations, including 58 patients in whom an air leak was present and 35 patients in whom an air leak was absent. In the testing phase, our software detected leak sites with a sensitivity and specificity of 81.3% and 68.9%, respectively.
UNASSIGNED: We have successfully developed a deep learning-based leak site detection application, which can be used in deflated lungs. Although the current version is still a prototype with a limited training data set, it is a novel concept of leak detection based entirely on visual information.

Gene Expression Data is the biological data to extract meaningful hidden information from the gene dataset. This gene information is used for disease diagnosis especially in cancer treatment based on the variations in gene expression levels. DNA microarray is an efficient method for gene expression classification and prediction of cancer disease for specific types of cancer. Due to the abundance of computing power, deep learning (DL) has become a widespread technique in the healthcare sector. The gene expression dataset has a limited number of samples but a large number of features. Data augmentation is needed for gene expression datasets to overcome the dimensionality problem in gene data. It is a technique to generating the synthetic samples to increase the diversity of data. Deep learning methods are designed to learn and extract the features that come from the raw input data in the form of multidimensional arrays. This paper reviews the existing research in deep learning techniques like Feed Forward Neural Network (FFN), Convolutional Neural Network (CNN), Autoencoder (AE) and Recurrent Neural Network (RNN) for the classification and prediction of cancer disease and its types through gene expression data analysis.