On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning.

Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user's experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge and improve the autonomy of prosthetics, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that an object's physical characteristics can affect the agent's ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object-gripping capabilities.

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning.

BACKGROUND: Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices. Therefore, this study develops an automatic and accurate image-based catheter segmentation method in cerebral angiography using deep learning. METHODS: Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error. RESULTS: The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view. CONCLUSIONS: These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce.

Non-invasive mid-term electrocardiogram patch monitoring is effective in detecting atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a cause of serious morbidity such as stroke. Early detection and treatment of AF is important. Current guidelines recommend screening via opportunistic pulse taking or 12­lead electrocardiogram. Mid-term ECG patch monitors increases the sensitivity of AF detection. METHODS: The Singapore Atrial Fibrillation Study is a prospective multi-centre study aiming to study the incidence of AF in patients with no prior AF and a CHA2DS2-VASc score of at least 1, with the use of a mid-term continuous ECG monitoring device (Spyder ECG). Consecutive patients from both inpatient and outpatient settings were recruited from 3 major hospitals from May 2016 to December 2019. RESULTS: Three hundred and fifty-five patients were monitored. 6 patients (1.7%) were diagnosed with AF. There were no significant differences in total duration of monitoring between the AF and non-AF group (6.39 ± 3.19 vs 5.42 ± 2.46 days, p = 0.340). Patients with newly detected AF were more likely to have palpitations (50.0% vs 11.8%, p = 0.027). Half of the patients (n = 3, 50.0%) were diagnosed on the first day of monitoring and the rest were diagnosed after 24 h. On univariate analysis, only hyperlipidemia was associated with reduced odds of being diagnosed with AF (OR HR 0.08 CI 0.01-0.74, p = 0.025). In a group of 128 patients who underwent coronary artery bypass grafting and had post-operative ECG monitoring, 9 patients (7.0%) were diagnosed with post-operative AF. CONCLUSIONS: The use of non-invasive mid-term patch-based ECG monitoring is an effective modality for AF screening.

Instinct to insight: Neural correlates of ethological strategy learning.

In ethological behaviors like parenting, animals innately follow stereotyped patterns of choices to decide between uncertain outcomes but can learn to modify their strategies to incorporate new information. For example, female mice in a T-maze instinctively use spatial-memory to search for pups where they last found them but can learn more efficient strategies employing pup-associated acoustic cues. We uncovered neural correlates for transitioning between these innate and learned strategies. Auditory cortex (ACx) was required during learning. ACx firing at the nest increased with learning and correlated with subsequent search speed but not outcome. Surprisingly, ACx suppression rather than facilitation during search was more prognostic of correct sound-cued outcomes - even before adopting a sound-cued strategy. Meanwhile medial prefrontal cortex encoded the last pup location, but this decayed as the spatial-memory strategy declined. Our results suggest a neural competition between a weakening spatial-memory and strengthening sound-cued neural representation to mediate strategy switches.

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Two data-driven algorithms were developed for detecting and characterizing Inferior Vena Cava (IVC) filters on abdominal computed tomography to assist healthcare providers with the appropriate management of these devices to decrease complications: one based on 2-dimensional data and transfer learning (2D + TL) and an augmented version of the same algorithm which accounts for the 3-dimensional information leveraging recurrent convolutional neural networks (3D + RCNN). The study contains 2048 abdominal computed tomography studies obtained from 439 patients who underwent IVC filter placement during the 10-year period from January 1st, 2009, to January 1st, 2019. Among these, 399 patients had retrievable filters, and 40 had non-retrievable filter types. The reference annotations for the filter location were obtained through a custom-developed interface. The ground truth annotations for the filter types were determined based on the electronic medical record and physician review of imaging. The initial stage of the framework returns a list of locations containing metallic objects based on the density of the structure. The second stage processes the candidate locations and determines which one contains an IVC filter. The final stage of the pipeline classifies the filter types as retrievable vs. non-retrievable. The computational models are trained using Tensorflow Keras API on an Nvidia Quadro GV100 system. We utilized a fine-tuning supervised training strategy to conduct our experiments. We find that the system achieves high sensitivity on detecting the filter locations with a high confidence value. The 2D + TL model achieved a sensitivity of 0.911 and a precision of 0.804, and the 3D + RCNN model achieved a sensitivity of 0.923 and a precision of 0.853 for filter detection. The system confidence for the IVC location predictions is high: 0.993 for 2D + TL and 0.996 for 3D + RCNN. The filter type prediction component of the system achieved 0.945 sensitivity, 0.882 specificity, and 0.97 AUC score with 2D + TL and 0. 940 sensitivity, 0.927 specificity, and 0.975 AUC score with 3D + RCNN. With the intent to create tools to improve patient outcomes, this study describes the initial phase of a computational framework to support healthcare providers in detecting patients with retained IVC filters, so an individualized decision can be made to remove these devices when appropriate, to decrease complications. To our knowledge, this is the first study that curates abdominal computed tomography (CT) scans and presents an algorithm for automated detection and characterization of IVC filters.

HViT: Hybrid vision inspired transformer for the assessment of carotid artery plaque by addressing the cross-modality domain adaptation problem in MRI.

BACKGROUND: Medical image classification is crucial for accurate and efficient diagnosis, and deep learning frameworks have shown significant potential in this area. When a general learning deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, which degrades the performance of deep learning models and leads to inaccurate predictions. PURPOSE: This study aims to propose a framework that utilized the cross-modality domain adaptation and accurately diagnose and classify MRI scans and domain knowledge into stable and vulnerable plaque categories by a modified Vision Transformer (ViT) model for the classification of MRI scans and transformer model for domain knowledge classification. METHODS: This study proposes a Hybrid Vision Inspired Transformer (HViT) framework that employs a convolutional layer for image pre-processing and normalization and a 3D convolutional layer to enable ViT to classify 3D images. Our proposed HViT framework introduces a slim design with a multi-branch network and channel attention, improving patch embedding extraction and information learning. Auxiliary losses target shallow features, linking them with deeper ones, enhancing information gain, and model generalization. Furthermore, replacing the MLP Head with RNN enables better backpropagation for improved performance. Moreover, we utilized a modified transformer model with LSTM positional encoding and Golve word vector to classify domain knowledge. By using ensemble learning techniques, specifically stacking ensemble learning with hard and soft prediction, we combine the predictive power of both models to address the cross-modality domain adaptation problem and improve overall performance. RESULTS: The proposed framework achieved an accuracy of 94.32% for carotid artery plaque classification into stable and vulnerable plaque by addressing the cross-modality domain adaptation problem and improving overall performance. CONCLUSION: The model was further evaluated using an independent dataset acquired from different hardware protocols. The results demonstrate that the proposed deep learning model significantly improves the generalization ability across different MRI scans acquired from different hardware protocols without requiring additional calibration data.

End to end vision transformer architecture for brain stroke assessment based on multi-slice classification and localization using computed tomography.

BACKGROUND: Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes. PURPOSE: In this study, we aimed to enhance the vision transformer architecture for the multi-slice classification of CT scans of each patient into three categories, including Normal, Infarction, Hemorrhage, and patient-wise stroke localization, based on end-to-end vision transformer architecture. This framework can provide an automated, objective, and consistent approach to stroke diagnosis and localization, enabling personalized treatment plans based on the location and extent of the stroke. METHODS: We modified the Vision Transformer (ViT) in combination with neural network layers for the multi-slice classification of brain CT scans of each patient into normal, infarction, and hemorrhage classes. For stroke localization, we used the ViT architecture and convolutional neural network layers to detect stroke and localize it by bounding boxes for infarction and hemorrhage regions in a patient-wise manner based on multi slices. RESULTS: Our proposed framework achieved an overall accuracy of 87.51% in classifying brain CT scan slices and showed high precision in localizing the stroke patient-wise. Our results demonstrate the potential of our method for accurate and reliable stroke diagnosis and localization. CONCLUSION: Our study enhanced ViT architecture for automated stroke diagnosis and localization using brain CT scans, which could have significant implications for stroke management and treatment. The use of deep learning algorithms can provide a more objective and consistent approach to stroke diagnosis and potentially enable personalized treatment plans based on the location and extent of the stroke. Further studies are needed to validate our method on larger and more diverse datasets and to explore its clinical utility in real-world settings.

The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective.

CONCEPTUAL INTRODUCTION: To introduce the concept of cybernetical intelligence, deep learning, development history, international research, algorithms, and the application of these models in smart medical image analysis and deep medicine are reviewed in this paper. This study also defines the terminologies for cybernetical intelligence, deep medicine, and precision medicine. REVIEW OF METHODS: Through literature research and knowledge reorganization, this review explores the fundamental concepts and practical applications of various deep learning techniques and cybernetical intelligence by conducting extensive literature research and reorganizing existing knowledge in medical imaging and deep medicine. The discussion mainly centers on the applications of classical models in this field and addresses the limitations and challenges of these basic models. EVALUATION AND DISCUSSIONS: In this paper, the more comprehensive overview of the classical structural modules in convolutional neural networks is described in detail from the perspective of cybernetical intelligence in deep medicine. The results and data of major research contents of deep learning are consolidated and summarized. CONCLUSION: There are some problems in machine learning internationally, such as insufficient research techniques, unsystematic research methods, incomplete research depth, and incomplete evaluation research. Some suggestions are given in our review to solve the problems existing in the deep learning models. Cybernetical intelligence has proven to be a valuable and promising avenue for advancing various fields, including deep medicine and personalized medicine.

BBox-Guided Segmentor: Leveraging expert knowledge for accurate stroke lesion segmentation using weakly supervised bounding box prior.

Stroke is one of the leading causes of death and disability in the world. Despite intensive research on automatic stroke lesion segmentation from non-invasive imaging modalities including diffusion-weighted imaging (DWI), challenges remain such as a lack of sufficient labeled data for training deep learning models and failure in detecting small lesions. In this paper, we propose BBox-Guided Segmentor, a method that significantly improves the accuracy of stroke lesion segmentation by leveraging expert knowledge. Specifically, our model uses a very coarse bounding box label provided by the expert and then performs accurate segmentation automatically. The small overhead of having the expert provide a rough bounding box leads to large performance improvement in segmentation, which is paramount to accurate stroke diagnosis. To train our model, we employ a weakly-supervised approach that uses a large number of weakly-labeled images with only bounding boxes and a small number of fully labeled images. The scarce fully labeled images are used to train a generator segmentation network, while adversarial training is used to leverage the large number of weakly-labeled images to provide additional learning signals. We evaluate our method extensively using a unique clinical dataset of 99 fully labeled cases (i.e., with full segmentation map labels) and 831 weakly labeled cases (i.e., with only bounding box labels), and the results demonstrate the superior performance of our approach over state-of-the-art stroke lesion segmentation models. We also achieve competitive performance as a SOTA fully supervised method using less than one-tenth of the complete labels. Our proposed approach has the potential to improve stroke diagnosis and treatment planning, which may lead to better patient outcomes.

Reliability of the National Institutes of Health (NIH) Stroke Scale Between Emergency Room and Neurology Physicians for Initial Stroke Severity Scoring.

INTRODUCTION: In patients with acute ischemic stroke (AIS), the National Institutes of Health Stroke Scale (NIHSS) is essential to establishing a patient's initial stroke severity. While previous research has validated NIHSS scoring reliability between neurologists and other clinicians, it has not specifically evaluated NIHSS scoring reliability between emergency room (ER) and neurology physicians within the same clinical scenario and timeframe in a large cohort of patients. This study specifically addresses the key question: does an ER physician's NIHSS score agree with the neurologist's NIHSS score in the same patient at the same time in a real-world context? METHODS: Data was retrospectively collected from 1,946 patients being evaluated for AIS at Houston Methodist Hospital from 05/2016 - 04/2018. Triage NIHSS scores assessed by both the ER and neurology providers within one hour of each other under the same clinical context were evaluated for comparison. Ultimately, 129 patients were included in the analysis. All providers in this study were NIHSS rater-certified. RESULTS: The distribution of the NIHSS score differences (ER score - neurology score) had a mean of -0.46 and a standard deviation of 2.11. The score difference between provider teams ranged ±5 points. The intraclass correlation coefficient (ICC) for the NIHSS scores between the ER and neurology teams was 0.95 (95% CI, 0.93 - 0.97) with an F-test of 42.41 and a p-value of 4.43E-69. Overall reliability was excellent between the ER and neurology teams. CONCLUSION: We evaluated triage NIHSS scores performed by ER and neurology providers under matching time and treatment conditions and found excellent interrater reliability. The excellent score agreement has important implications for treatment decision-making during patient handoff and further in stroke modeling, prediction, and clinical trial registries where missing NIHSS scores may be equivalently substituted from either provider team.

Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI.

BACKGROUND AND OBJECTIVE: Traditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the corpus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy. Residual learning can facilitate network training, Bi-Directional Convolutional LSTM (BDC-LSTM) can exploit interlayer spatial dependencies, and HDC can expand the receptive domain without losing resolution. METHODS: In this paper, we propose a segmentation method by combining BDC-LSTM and U-Net to segment the corpus callosum from multiple angles of brain images based on computed tomography (CT) and magnetic resonance imaging (MRI) in which two types of sequence, namely T2-weighted imaging as well as the Fluid Attenuated Inversion Recovery (Flair), were utilized. The two-dimensional slice sequences are segmented in the cross-sectional plane, and the segmentation results are combined to obtain the final results. Encoding, BDC- LSTM, and decoding include convolutional neural networks. The coding part uses asymmetric convolutional layers of different sizes and dilated convolutions to get multi-slice information and extend the convolutional layers' perceptual field. RESULTS: This paper uses BDC-LSTM between the encoding and decoding parts of the algorithm. On the image segmentation of the brain in multiple cerebral infarcts dataset, accuracy rates of 0.876, 0.881, 0.887, and 0.912 were attained for the intersection of union (IOU), dice similarity coefficient (DS), sensitivity (SE), and predictive positivity value (PPV). The experimental findings demonstrate that the algorithm outperforms its rivals in accuracy. CONCLUSION: This paper obtained segmentation results for three images using three models, ConvLSTM, Pyramid-LSTM, and BDC-LSTM, and compared them to verify that BDC-LSTM is the best method to perform the segmentation task for faster and more accurate detection of 3D medical images. We improve the convolutional neural network segmentation method to obtain medical images with high segmentation accuracy by solving the over-segmentation problem.

Functional magnetic resonance imaging providing the brain effect mechanism of acupuncture and moxibustion treatment for depression.

The efficacy of acupuncture and moxibustion in the treatment of depression has been fully recognized internationally. However, its central mechanism is still not developed into a unified standard, and it is generally believed that the central mechanism is regulation of the cortical striatum thalamic neural pathway of the limbic system. In recent years, some scholars have applied functional magnetic resonance imaging (fMRI) to study the central mechanism and the associated brain effects of acupuncture and moxibustion treatment for depression. This study reviews the acupuncture and moxibustion treatment of depression from two aspects: (1) fMRI study of the brain function related to the acupuncture treatment of depression: different acupuncture and moxibustion methods are summarized, the fMRI technique is elaborately explained, and the results of fMRI study of the effects of acupuncture are analyzed in detail, and (2) fMRI associated "brain functional network" effects of acupuncture and moxibustion on depression, including the effects on the hippocampus, the amygdala, the cingulate gyrus, the frontal lobe, the temporal lobe, and other brain regions. The study of the effects of acupuncture on brain imaging is not adequately developed and still needs further improvement and development. The brain function networks associated with the acupuncture treatment of depression have not yet been adequately developed to provide a scientific and standardized mechanism of the effects of acupuncture. For this purpose, this study analyzes in-depth the clinical studies on the treatment of anxiety and depression by acupuncture and moxibustion, by depicting how the employment of fMRI technology provides significant imaging changes in the brain regions. Therefore, the study also provides a reference for future clinical research on the treatment of anxiety and depression.

KFPredict: An ensemble learning prediction framework for diabetes based on fusion of key features.

BACKGROUND AND OBJECTIVE: Diabetes is a disease that requires early detection and early treatment, and complications are likely to occur in late stages of the disease, threatening the life of patients. Therefore, in order to diagnose diabetic patients as early as possible, it is necessary to establish a model that can accurately predict diabetes. METHODOLOGY: This paper proposes an ensemble learning framework: KFPredict, which combines multi-input models with key features and machine learning algorithms. We first propose a multi-input neural network model (KF_NN) that fuses key features and uses a decision tree-based selection recursive feature elimination algorithm and correlation coefficient method to screen out the key feature inputs and secondary feature inputs in the model. We then ensemble KF_NN with three machine learning algorithms (i.e., Support Vector Machine, Random Forest and K-Nearest Neighbors) for soft voting to form our predictive classifier for diabetes prediction. RESULTS: Our framework demonstrates good prediction results on the test set with a sensitivity of 0.85, a specificity of 0.98, and an accuracy of 93.5%. Compared with the single prediction method KFPredict, the accuracy is up to 18.18% higher. Concurrently, we also compared KFPredict with the existing prediction methods. It still has good prediction performance, and the accuracy rate is improved by up to 14.93%. CONCLUSION: This paper constructs a diabetes prediction framework that combines multi-input models with key features and machine learning algorithms. Taking tthe PIMA diabetes dataset as the test data, the experiment shows that the framework presents good prediction results.

Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network.

OBJECTIVE: The traditional ICM is widely used in applications, such as image edge detection and image segmentation. However, several model parameters must be set, which tend to lead to reduced accuracy and increased cost. As medical images have more complex edges, contours and details, more suitable combinatorial algorithms are needed to handle the pathological diagnosis of multiple cerebral infarcts and acute strokes, resulting in the findings being more applicable, as well as having good clinical value. METHODS: To better solve the medical image fusion and diagnosis problems, this paper introduces the image fusion algorithm based on the combination of NSCT and improved ICM and proposes low-frequency, sub-band fusion rules and high-frequency sub-band fusion rules. The above method is applied to the fusion of CT/MRI images, subsequently, three other fusion algorithms, including NSCT-SF-PCNN, NSCT-SR-PCNN and Adaptive-PCNN are compared, and the simulation results of image fusion are analyzed and validated. RESULTS: According to the experimental findings, the suggested algorithm performs better than other fusion algorithms in terms of five objective evaluation metrics or subjective evaluation. The NSCT transform and the improved ICM were combined, and the outcomes were evaluated against those of other fusion algorithms. The CT/MRI medical images of healthy brain tissue, numerous cerebral infarcts and acute strokes were combined using this technique. CONCLUSION: Medical image fusion using Adaptive-PCNN produces satisfactory results, not only in relation to improved image clarity but also in terms of outstanding edge information, high contrast and brightness.

Validation of MyDiagnostick tool to identify atrial fibrillation in a multi-ethnic Asian population.

Introduction: MyDiagnostick is an atrial fibrillation (AF) screening tool that has been validated in the Caucasian population in the primary care setting. Methods: In our study, we compared MyDiagnostick with manual pulse check for AF screening in the community setting. Results: In our cohort of 671 candidates from a multi-ethnic Asian population, AF prevalence was found to be 1.78%. Of 12 candidates, 6 (50.0%) had a previous history of AF and another 6 (50.0%) were newly diagnosed with AF. Candidates found to have AF during the screening were older (72.0 ± 11.7 years vs. 56.0 ± 13.0 years, P < 0.0001) and had a higher CHADSVASC risk score (2.9 ± 1.5 vs. 1.5 ± 1.1, P = 0.0001). MyDiagnostick had a sensitivity of 100.0% and a specificity of 96.2%. In comparison, manual pulse check had a sensitivity of 83.3% and a specificity of 98.9%. Conclusion: MyDiagnostick is a simple AF screening device that can be reliably used by non-specialist professionals in the community setting. Its sensitivity and specificity are comparable and validated across various studies performed in different population cohorts.

A single-centre experience of His bundle pacing without electrophysiological mapping system: implant success rate, safety, pacing characteristics and one-year follow up.

Introduction: Despite the challenges related to His bundle pacing (HBP), recent data suggest an improved success rate with experience. As a non-university, non-electrophysiology specialised centre in Singapore, we report our experiences in HBP using pacing system analyser alone. Methods: Data of 28 consecutive patients who underwent HBP from August 2018 to February 2019 was retrospectively obtained. The clinical and technical outcomes of these patients were compared between two timeframes of three months each. Patients were followed up for 12 months. Results: Immediate technical success was achieved in 21 (75.0%) patients (mean age 73.3 ± 10.7 years, 47.6% female). The mean left ventricular ejection fraction was 53.9% ± 12.1%. The indications for HBP were atrioventricular block (n = 13, 61.9%), sinus node dysfunction (n = 7, 33.3%) and upgrade from implantable cardioverter-defibrillator to His-cardiac resynchronisation therapy (n = 1, 4.8%). No significant difference was observed in baseline characteristics between Timeframe 1 and Timeframe 2. Improvements pertaining to mean fluoroscopy time were achieved between the two timeframes. There was one HBP-related complication of lead displacement during Timeframe 1. All patients with successful HBP achieved non-selective His bundle (NSHB) capture, whereas only eight patients had selective His bundle (SHB) capture. NSHB and SHB capture thresholds remained stable at the 12-month follow-up. Conclusion: Permanent HBP is feasible and safe, even without the use of an electrophysiology recording system. This was successfully achieved in 75% of patients, with no adverse clinical outcomes during the follow-up period.

Unsupervised domain adaptive myocardial infarction MRI classification diagnostics model based on target domain confused sample resampling.

OBJECTIVE: Inefficient circulatory system due to blockage of blood vessels leads to myocardial infarction and acute blockage. Myocardial infarction is frequently classified and diagnosed in medical treatment using MRI, yet this method is ineffective and prone to error. As a result, there are several implementation scenarios and clinical significance for employing deep learning to develop computer-aided algorithms to aid cardiologists in the routine examination of cardiac MRI. METHODS: This research uses two distinct domain classifiers to address this issue and achieve domain adaptation between the particular field and the specific part is a problem Current research on environment adaptive systems cannot effectively obtain and apply classification information for unsupervised scenes of target domain images. Insufficient information interchange between specific domains and specific domains is a problem. In this study, two different domain classifiers are used to solve this problem and achieve domain adaption. To effectively mine the source domain images for classification understanding, an unsupervised MRI classification technique for myocardial infarction called CardiacCN is proposed, which relies on adversarial instructions related to the interpolation of confusion specimens in the target domain for the conflict of confusion specimens for the target domain classification task. RESULTS: The experimental results demonstrate that the CardiacCN model in this study performs better on the six domain adaption tasks of the Sunnybrook Cardiac Dataset (SCD) dataset and increases the mean target area myocardial infarction MRI classification accuracy by approximately 1.2 percent. The classification performance of the CardiacCN model on the target domain does not vary noticeably when the temperature-controlled duration hyper-parameter rl falls in the region of 5-30. According to the experimental findings, the CardiacCN model is more resistant to the excitable rl. The CardiacCN model suggested in this research may successfully increase the accuracy of the source domain predictor for the target domain myocardial infarction clinical scanning classification in unsupervised learning, as shown by the visualization analysis infrastructure provision nurture. It is evident from the visualization assessment of embedded features that the CardiacCN model may significantly increase the source domain classifier's accuracy for the target domain's classification of myocardial infarction in clinical scans under unsupervised conditions. CONCLUSION: To address misleading specimens with the inconsistent classification of target-domain myocardial infarction medical scans, this paper introduces the CardiacCN unsupervised domain adaptive MRI classification model, which relies on adversarial learning associated with resampling target-domain confusion samples. With this technique, implicit image classification information from the target domain is fully utilized, knowledge transfer from the target domain to the specific domain is encouraged, and the classification effect of the myocardial ischemia medical scan is improved in the target domain of the unsupervised scene.

DE-MRI myocardial fibrosis segmentation and classification model based on multi-scale self-supervision and transformer.

OBJECTIVE: The segmentation and categorization of fibrotic tissue in time-lapse enhanced MRI scanning are quite challenging, and it is mainly done manually for myocardial DE-MRI images. On the other hand, DE-MRI instructions for segmenting and classifying cardiac hypertrophy are complex and prone to inaccuracy. Developing cardiac DE-MRI classification and prediction methods is crucial. METHODS: This paper introduces a self-supervised myocardial histology segmentation algorithm with multi-scale portrayal consistency to address the degree of sophistication of cardiology DE-MRI. The model retrieves multi-scale representations from multiple expanded viewpoints using a Siamese system and uses resemblance learning instruction to achieve unlabeled representations. The DE-MRI data train the network weights to generate a superior segmentation effect by accurately reflecting the exact scale information. The paper provides an end-to-end method for detecting myocardial fibrosis tissue using a Transformer as a result of the poor classification outcomes of myocardial fibrosis substance in DE-MRI. A deep learning model is created using the Pre-LN Transformer decoded simultaneously with the Multi-Scale Transformer backbone structure developed in this paper. In addition, the joint regression cost, which incorporates the CIoU Loss and the L1 Loss, is used to determine the distance between forecast blocks and labels. RESULTS: Increasing the independent evaluation and annotations position compared enhances performance compared to the segmentation method without canvas matching by 1.76%, 1.27%, 0.93%, and -1.17 mm on Dice, PPV, SEN, and HD, respectively. Based on the strongest of the three single-scale representation methodologies, the segmentation model in this study is enhanced by 0.71%, 0.79%, and 1.47%, as well as -1.49 mm on Dice, PPV, SEN, and HD, respectively. The effectiveness and reliability of the segmentation model are confirmed. Additionally, testing results show that this study's recognition system's mAP is 84.97%, which is greater than the benchmark techniques used in most other studies. The framework converges round is compressed by 18.1% compared to the DETR detection approach, and the identification rate is improved by 3.5%, proving the strategy's value. CONCLUSION: The self-supervised cardiac fibrosis segmentation method with multi-scale portrayal consistency and end-to-end myocardial histology categorization is introduced in this study. To solve the challenges of segmentation and myocardial fibrosis identification in cardiology DE-MRI, a Transformer-based detection approach is put forth. It may address the issue of the myocardial scarring material's low accuracy in segmentation and classification in DE-MRI, as well as provide clinicians with a fibrosis diagnosis that is supplementary to the conventional therapy of heart ailments.

Deep learning-based medical image segmentation of the aorta using XR-MSF-U-Net.

PURPOSE: This paper proposes a CT images and MRI segmentation technology of cardiac aorta based on XR-MSF-U-Net model. The purpose of this method is to better analyze the patient's condition, reduce the misdiagnosis and mortality rate of cardiovascular disease in inhabitants, and effectively avoid the subjectivity and unrepeatability of manual segmentation of heart aorta, and reduce the workload of doctors. METHOD: We implement the X ResNet (XR) convolution module to replace the different convolution kernels of each branch of two-layer convolution XR of common model U-Net, which can make the model extract more useful features more efficiently. Meanwhile, a plug and play attention module integrating multi-scale features Multi-scale features fusion module (MSF) is proposed, which integrates global local and spatial features of different receptive fields to enhance network details to achieve the goal of efficient segmentation of cardiac aorta through CT images and MRI. RESULTS: The model is trained on common cardiac CT images and MRI data sets and tested on our collected data sets to verify the generalization ability of the model. The results show that the proposed XR-MSF-U-Net model achieves a good segmentation effect on CT images and MRI. In the CT data set, the XR-MSF-U-Net model improves 7.99% in key index DSC and reduces 11.01 mm in HD compared with the benchmark model U-Net, respectively. In the MRI data set, XR-MSF-U-Net model improves 10.19% and reduces 6.86 mm error in key index DSC and HD compared with benchmark model U-Net, respectively. And it is superior to similar models in segmentation effect, proving that this model has significant advantages. CONCLUSION: This study provides new possibilities for the segmentation of aortic CT images and MRI, improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the segmentation of aortic CT images and MRI.

Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism.

OBJECTIVE: It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI. METHODS: Firstly, an end-to-end minimally supervised and semi-supervised semantic DE-MRI myocardial fibrosis segmentation framework is proposed, which combines image classification and semantic segmentation branches based on the self-attention mechanism. Following that, a residual hole network fused with the dual attention mechanism was built, and a double attention metabolic pathway classification method for cardiac fibrosis in DE-MRI images was developed. RESULTS: By adding pixel-level labels to an extra 40 training images, the segmentation model may enhance semantic segmentation performance by 2.6 percent (from 61.2 percent to 63.8 percent). When the number of pixel-level labels is increased to 80, semi-supervised feature extraction increases by 4.7 percent when compared to weakly guided semantic segmentation. Adding an attention mechanism to the critical network DRN (Deep Residual Network) can increase the classifier's performance by a small amount. Experiments revealed that the models worked effectively. CONCLUSION: This paper investigates the segmentation and classification of cardiac fibrosis in DE-MRI data using a semi-supervised semantic segmentation and dual attention mechanism, dealing with the issue that existing segmentation algorithms have difficulty segmenting myocardial fibrosis tissue. In the future, we can consider optimizing the design of the attention module to reduce the module computation.