Lower back pain is one of the major global challenges in health problems. Medical imaging is rapidly taking a predominant position for the diagnosis and treatment of lower back abnormalities. Magnetic resonance imaging (MRI) is a primary tool for detecting anatomical and functional abnormalities in the intervertebral disc (IVD) and provides valuable data for both diagnosis and research. Deep learning methods perform well in computer visioning when labeled general image training data are abundant. In the practice of medical images, the labeled data or the segmentation data are produced manually. However, manual medical image segmentation leads to two main issues: much time is needed for delineation, and reproducibility is called into question. To handle this problem, we developed an automated approach for IVD instance segmentation that can utilize T1 and T2 images during this study to handle data limitation problems and computational time problems and improve the generalization of the algorithm. This method builds upon mask-RCNN; we proposed a multistage optimization mask-RCNN (MOM-RCNN) for deep learning segmentation networks. We used a multi-optimization training system by utilizing stochastic gradient descent and adaptive moment estimation (Adam) with T1 and T2 in MOM-RCNN. The proposed method showed a significant improvement in processing time and segmentation results compared to previous commonly used segmentation methods. We evaluated the results using several different key performance measures. We obtain the Dice coefficient (99%). Our method can define the IVD’s segmentation as much as 88% (sensitivity) and recognize the non-IVD as much as 98% (specificity). The results also obtained increasing precision (92%) with a low global consistency error (0.03), approaching 0 (the best possible score). On the spatial distance measures, the results show a promising reduction from 0.407 ± 0.067 mm in root mean square error to 0.095 ± 0.026 mm, Hausdorff distance from 12.313 ± 3.015 to 5.155 ± 1.561 mm, and average symmetric surface distance from 1.944 ± 0.850 to 0.49 ± 0.23 mm compared to other state-of-the-art methods. We used MRI images from 263 patients to demonstrate the efficiency of our proposed method.

The clinical application of a real-time artificial intelligence (AI) image processing system to diagnose upper gastrointestinal (GI) malignancies remains an experimental research and engineering problem. Understanding these commonly used technical techniques is required to appreciate the scientific quality and novelty of AI studies. Clinicians frequently lack this technical background, and AI experts may be unaware of such clinical relevance and implications in daily practice. As a result, there is a growing need for a multidisciplinary, international assessment of how to conduct high-quality AI research in upper GI malignancy detection. This research will help endoscopists build approaches or models to increase diagnosis accuracy for upper GI malignancies despite variances in experience, education, personnel, and resources, as it offers real-time and retrospective chances to improve upper GI malignancy diagnosis and screening. This comprehensive review sheds light on potential enhancements to computer-aided diagnostic (CAD) systems for GI endoscopy. The survey includes 65 studies on automatic upper GI malignancy diagnosis and evaluation, which are compared by endoscopic modalities, image counts, models, validation methods, and results. The main goal of this research is to assess and compare each AI method’s current stage and potential improvement to boost performance, maturity, and the possibility to open new research areas for the application of a real-time AI image recognition system that diagnoses upper GI malignancies. The findings of this study suggest that Support Vector Machines (SVM) are frequently utilized in gastrointestinal (GI) image processing within the context of machine learning (ML). Moreover, the analysis reveals that CNN-based supervised learning object detection models are widely employed in GI image analysis within the deep learning (DL) context. The results of this study also suggest that RGB is the most commonly used image modality for GI analysis, with color playing a vital role in detecting bleeding locations. Researchers rely on public datasets from 2018-2019 to develop AI systems, but combining them is challenging due to their unique classes. To overcome the problem of insufficient data to train a new DL model, a standardized database is needed to hold different datasets for the development of AI-based GI endoscopy systems.

Detecting and preventing industrial machine failures are significant in the modern manufacturing industry because machine failures substantially increase both maintenance and manufacturing costs. Recently, state-of-the-art deep learning techniques that use acoustic signals have been widely applied to solve industrial machine malfunction detection problems in order to reduce maintenance and manufacturing costs. The authors of this research propose a deep learning-based industrial machine malfunction detection model that uses acoustic signals to classify normal and abnormal conditions of industrial machines. In particular, a weighted ensemble model based on EfficientNet-B0, B5, and B7 is considered to improve classification performance. Case studies involving an open dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII) validate that the proposed EfficientNet-based weighted ensemble model provides better classification performance than individual classifiers and other ensemble models.

The creation of 3D models for cardiac mapping systems is time-consuming, and the models suffer from issues with repeatability among operators. The present study aimed to construct a double-shaped model composed of the left ventricle and left atrium. We developed cascaded-regression-based segmentation software with probabilistic point and appearance correspondence. Group-wise registration of point sets constructs the point correspondence from probabilistic matches, and the proposed method also calculates appearance correspondence from these probabilistic matches. Final point correspondence of group-wise registration constructed independently for three surfaces of the double-shaped model. Stochastic appearance selection of cascaded regression enables the effective construction in the aspect of memory usage and computation time. The two correspondence construction methods of active appearance models were compared in terms of the paired segmentation of the left atrium (LA) and left ventricle (LV). The proposed method segmented 35 cardiac CTs in six-fold cross-validation, and the symmetric surface distance (SSD), Hausdorff distance (HD), and Dice coefficient (DC), were used for evaluation. The proposed method produced 1.88 ± 0.37 mm of LV SSD, 2.25 ± 0.51 mm* of LA SSD, and 2.06 ± 0.34 mm* of the left heart (LH) SSD. Additionally, DC was 80.45% ± 4.27%***, where * $p<$ 0.05, ** $p<$ 0.01, and *** $p<$ 0.001. All p values derive from paired t-tests comparing iterative closest registration with the proposed method. In conclusion, the authors developed a cascaded regression framework for 3D cardiac CT segmentation.