Kidney Level Lupus Nephritis Classification Using Uncertainty Guided Bayesian Convolutional Neural Networks

Application of visual transformer in renal image analysis

Deep Self-Attention Network (Transformer) is an encoder-decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients' lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.

Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators

Diagnosis of diabetic kidney disease (DKD) mainly relies on screening the morphological variations and internal lesions of glomeruli from pathological kidney biopsy. The prominent pathological alterations of glomeruli for DKD include glomerular hypertrophy and nodular mesangial sclerosis. However, the qualitative judgment of these alterations is inaccurate and inconstant due to the intra- and inter-subject variability of pathologists. It is necessary to design artificial intelligence (AI) methods for accurate quantification of these pathological alterations and outcome prediction of DKD. In this work, we present an AI-driven framework to quantify the volume of glomeruli and degree of nodular mesangial sclerosis, respectively, based on an instance segmentation module and a novel weakly supervised Macro-Micro Aggregation (MMA) module. Subsequently, we construct classic machine learning models to predict the degree of DKD based on three selected pathological indicators via factor analysis. These corresponding modules are trained and tested on a total of 281 whole slide images (WSIs) digitized from two hospitals with different scanners. Our designed AI framework achieved inspiring results with 0.926 mIoU for glomerulus segmentation, and 0.899 F1 score for glomerulus classification in the external testing dataset. Meantime, the visualized results of the MMA module could reflect the location of the lesions. The performance of predicting disease achieved the F1 score of 0.917, which further proved the effectiveness of our AI-driven quantification of pathological indicators. Additionally, the interpretation of the machine learning model with the SHAP method showed similar accordance with the development of DKD in pathology. In conclusion, the proposed auxiliary diagnostic technologies have the feasibility for quantitative analysis of glomerular pathological tissues and alterations in DKD. Pathological quantitative indicators will also make it more convenient to provide doctors with assistance in clinical practice.

Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Early diagnosis is currently the most effective way to save the lives of patients with SLE. But it is very difficult to detect in the early stages of the disease. Because of this, this study proposes a machine learning system to help diagnose patients with SLE. To carry out the research, the extreme gradient boosting method has been implemented due to its performance characteristics, as it allows high performance, scalability, accuracy, and low computational load. From this method we try to recognize patterns in the data obtained from patients, which allow the classification of SLE patients with high accuracy and differentiate these patients from controls. Several machine learning methods have been analyzed in this study. The proposed method achieves a higher prediction value of patients who may suffer from SLE than the rest of the compared systems. The proposed algorithm achieved an improvement in accuracy of 4.49% over k-Nearest Neighbors. As for the Support Vector Machine and Gaussian Naive Bayes (GNB) methods, they achieved a lower performance than the proposed one, reaching values of 83% and 81%, respectively. It should be noted that the proposed system showed a higher area under the curve (90%) and a balanced accuracy (90%) than the other machine learning methods. This study shows the usefulness of ML techniques for identifying and predicting SLE patients. These results demonstrate the possibility of developing automatic diagnostic support systems for SLE patients based on machine learning techniques.

Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network

The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

PURPOSE OF REVIEW

Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care.

RECENT FINDINGS

The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists.

SUMMARY

Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.

Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects

Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.

Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks

Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject.

Uncertainty-guided graph attention network for parapneumonic effusion diagnosis

Parapneumonic effusion (PPE) is a common condition that causes death in patients hospitalized with pneumonia. Rapid distinction of complicated PPE (CPPE) from uncomplicated PPE (UPPE) in Computed Tomography (CT) scans is of great importance for the management and medical treatment of PPE. However, UPPE and CPPE display similar appearances in CT scans, and it is challenging to distinguish CPPE from UPPE via a single 2D CT image, whether attempted by a human expert, or by any of the existing disease classification approaches. 3D convolutional neural networks (CNNs) can utilize the entire 3D volume for classification: however, they typically suffer from the intrinsic defect of over-fitting. Therefore, it is important to develop a method that not only overcomes the heavy memory and computational requirements of 3D CNNs, but also leverages the 3D information. In this paper, we propose an uncertainty-guided graph attention network (UG-GAT) that can automatically extract and integrate information from all CT slices in a 3D volume for classification into UPPE, CPPE, and normal control cases. Specifically, we frame the distinction of different cases as a graph classification problem. Each individual is represented as a directed graph with a topological structure, where vertices represent the image features of slices, and edges encode the spatial relationship between them. To estimate the contribution of each slice, we first extract the slice representations with uncertainty, using a Bayesian CNN: we then make use of the uncertainty information to weight each slice during the graph prediction phase in order to enable more reliable decision-making. We construct a dataset consisting of 302 chest CT volumetric data from different subjects (99 UPPE, 99 CPPE and 104 normal control cases) in this study, and to the best of our knowledge, this is the first attempt to classify UPPE, CPPE and normal cases using a deep learning method. Extensive experiments show that our approach is lightweight in demands, and outperforms accepted state-of-the-art methods by a large margin. Code is available at https://github.com/iMED-Lab/UG-GAT.

Deep Learning-Based Artificial Intelligence System for Automatic Assessment of Glomerular Pathological Findings in Lupus Nephritis

Accurate assessment of renal histopathology is crucial for the clinical management of patients with lupus nephritis (LN). However, the current classification system has poor interpathologist agreement. This paper proposes a deep convolutional neural network (CNN)-based system that detects and classifies glomerular pathological findings in LN. A dataset of 349 renal biopsy whole-slide images (WSIs) (163 patients with LN, periodic acid-Schiff stain, 3906 glomeruli) annotated by three expert nephropathologists was used. The CNN models YOLOv4 and VGG16 were employed to localise the glomeruli and classify glomerular lesions (slight/severe impairments or sclerotic lesions). An additional 321 unannotated WSIs from 161 patients were used for performance evaluation at the per-patient kidney level. The proposed model achieved an accuracy of 0.951 and Cohen's kappa of 0.932 (95% CI 0.915-0.949) for the entire test set for classifying the glomerular lesions. For multiclass detection at the glomerular level, the mean average precision of the CNN was 0.807, with 'slight' and 'severe' glomerular lesions being easily identified (F1: 0.924 and 0.952, respectively). At the per-patient kidney level, the model achieved a high agreement with nephropathologist (linear weighted kappa: 0.855, 95% CI: 0.795-0.916, p < 0.001; quadratic weighted kappa: 0.906, 95% CI: 0.873-0.938, p < 0.001). The results suggest that deep learning is a feasible assistive tool for the objective and automatic assessment of pathological LN lesions.

DropConnect is effective in modeling uncertainty of Bayesian deep networks

Deep neural networks (DNNs) have achieved state-of-the-art performance in many important domains, including medical diagnosis, security, and autonomous driving. In domains where safety is highly critical, an erroneous decision can result in serious consequences. While a perfect prediction accuracy is not always achievable, recent work on Bayesian deep networks shows that it is possible to know when DNNs are more likely to make mistakes. Knowing what DNNs do not know is desirable to increase the safety of deep learning technology in sensitive applications; Bayesian neural networks attempt to address this challenge. Traditional approaches are computationally intractable and do not scale well to large, complex neural network architectures. In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights. This method called Monte Carlo DropConnect (MC-DropConnect) gives us a tool to represent the model uncertainty with little change in the overall model structure or computational cost. We extensively validate the proposed algorithm on multiple network architectures and datasets for classification and semantic segmentation tasks. We also propose new metrics to quantify uncertainty estimates. This enables an objective comparison between MC-DropConnect and prior approaches. Our empirical results demonstrate that the proposed framework yields significant improvement in both prediction accuracy and uncertainty estimation quality compared to the state of the art.