Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

Urinary renal tubular epithelial cells and casts as predictors of renal outcomes in patients with biopsy-proven diabetic nephropathy

BACKGROUND

Urine sediment examination is a time-tested and non-invasive diagnostic tool. This study investigated the characteristics of urine sediment and its association with severity and renal outcomes in diabetic nephropathy (DN) patients.

METHODS

A total of 201 biopsy-proven diabetic nephropathy patients (according to the pathological classification of diabetic nephropathy proposed by the Renal Pathology Society in 2010) who underwent manual urine sediment microscopic examination were included. We compared the clinicopathological characteristics of diabetic nephropathy patients with and without urinary renal tubular epithelial cells (RTECs) or renal tubular epithelial cell casts. The predictive value of urinary renal tubular epithelial cells or renal tubular epithelial cell casts for renal outcomes in diabetic nephropathy was analyzed.

RESULTS

Fifty of 201 (24.9%) diabetic nephropathy patients had renal tubular epithelial cells or renal tubular epithelial cell casts in urine sediment. Diabetic nephropathy patients with renal tubular epithelial cells or renal tubular epithelial cell casts in urine sediment had a significantly higher level of proteinuria [6.0 (3.1, 9.7) vs. 3.6 (1.8, 6.8) g/24 h, p = 0.001], higher serum creatinine [227.9 (151.6, 338.1) vs. 177.0 (104.4, 288.4) μmol/L, p = 0.016] and lower estimated glomerular filtration rate (eGFR) [25.8 (15.8, 44.8) vs. 35.7 (19.9, 65.0) mL/min/1.73 m2, p = 0.025] than those without. Cox regression analysis demonstrated that the presence of urinary renal tubular epithelial cells or renal tubular epithelial cell casts was independently associated with the development of end-stage kidney disease (ESKD) in diabetic nephropathy patients [HR 1.670, 95% CI (1.042, 2.676), p = 0.033]. Adding the presence of urinary renal tubular epithelial cells or renal tubular epithelial cell casts to the predictive model could improve the effectiveness of the model for predicting the risk of ESKD within one year after renal biopsy.

CONCLUSIONS

The presence of urinary renal tubular epithelial cells or renal tubular epithelial cell casts was associated with more severe kidney injury and worse renal outcomes in patients with diabetic nephropathy, thus perhaps providing a noninvasive biomarker for predicting diabetic nephropathy.

Artificial intelligence in the clinical laboratory

Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.

Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review

With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.

Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases

It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.

Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization

The diagnosis of kidney disease often involves analysing urine sediment particles. Traditionally, urinalysis was performed manually by collecting urine samples and using a centrifuge, which was prone to manual errors and relied on labour-intensive processes. Automated urine sediment microscopy, based on machine learning models, requires segmentation and feature extraction, which can hinder model performance due to intrinsic characteristics of microscopic images. Deep learning models based on convolutional neural networks (CNNs) often rely on a large number of manually annotated data, making the system computationally complex. This study propose an advanced deep learning model based on YOLOv5, which offers faster performance and requires comparatively less data. The proposed model used five variants of the YOLOv5 model (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) to detect six categories of urine particles (erythrocyte, leukocyte, crystals, cast, mycete, epithelial cells) from microscopic urine sediment images. The dataset involved 5376 images of urine sediments with 6 particles. There are 30 sets of hyperparamreteres are employed in the YOLOv5 model. To optimize the hyperparameters and fine-tune with the urine sediment dataset and for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among the six categories of detected particles mycete achieved maximum performance with a mAP of 97.6 % and crystals achieved minimum performance with a mAP of 81.7 % with YOLOv5x model compared to other particles. To optimize the hyperparameters for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among all the models, YOLOv5l and YOLOv5x performed the best. YOLOv5l achieved a mean average precision (mAP) of 85.8 % while YOLOv5x achieved a mAP of 85.4 % at an IoU threshold of 0.5. The detection speed per image was 23.4 ms for YOLOv5l and 28.4 ms for YOLOv5x. The proposed method developed a faster and better automated microscopic model using advanced deep learning techniques to detect urinary particles from microscopic urine sediment images for kidney disease identification. The method demonstrated strong performance in urinalysis.