Kayser-Fleischer ring

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Objectives

Wilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods.

Methods

The study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms.

Results

In this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur.

Conclusions

To sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.

A grading method for Kayser Fleischer ring images based on ResNet

The corneal K-F ring is the most common ophthalmic manifestation of WD patients. Early diagnosis and treatment have an important impact on the patient's condition. K-F ring is one of the gold standards for the diagnosis of WD disease. Therefore, this paper mainly focused on the detection and grading of the K-F ring. The aim of this study is three-fold. Firstly, to create a meaningful database, the K-F ring images are collected which contains 1850 images with 399 different WD patients, and then this paper uses the chi-square test and Friedman test to analyze the statistical significance. Subsequently, the all collected images were graded and labeled with an appropriate treatment approach, as a result, these images could be used to detect the corneal through the YOLO. After the detection of corneal, image segmentation was realized in batches. Finally, in this paper, different deep convolutional neural networks (VGG, ResNet, and DenseNet) were used to realize the grading of the K-F ring images in the KFID. Experimental results reveal that the entire pre-trained models obtain excellent performance. The global accuracies achieved by the six models i.e., VGG-16, VGG-19, ResNet18, ResNet34, ResNet50, and DenseNet are 89.88%, 91.89%, 94.18%, 95.31%, 93.59%, and 94.58% respectively. ResNet34 displayed the highest recall, specificity, and F1-score of 95.23%, 96.99%, and 95.23%. DenseNet showed the best precision of 95.66%. As such, the findings are encouraging, demonstrating the effectiveness of ResNet in the automatic grading of the K-F ring. Moreover, it provides effective help for the clinical diagnosis of HLD.

Clinical and Forensic Signs Resulting from Exposure to Heavy Metals and Other Chemical Elements of the Periodic Table

Several heavy metals and other chemical elements are natural components of the Earth's crust and their properties and toxicity have been recognized for thousands of years. Moreover, their use in industries presents a major source of environmental and occupational pollution. Therefore, this ubiquity in daily life may result in several potential exposures coming from natural sources (e.g., through food and water contamination), industrial processes, and commercial products, among others. The toxicity of most chemical elements of the periodic table accrues from their highly reactive nature, resulting in the formation of complexes with intracellular compounds that impair cellular pathways, leading to dysfunction, necrosis, and apoptosis. Nervous, gastrointestinal, hematopoietic, renal, and dermatological systems are the main targets. This manuscript aims to collect the clinical and forensic signs related to poisoning from heavy metals, such as thallium, lead, copper, mercury, iron, cadmium, and bismuth, as well as other chemical elements such as arsenic, selenium, and fluorine. Furthermore, their main sources of occupational and environmental exposure are highlighted in this review. The importance of rapid recognition is related to the fact that, through a high degree of suspicion, the clinician could rapidly initiate treatment even before the toxicological results are available, which can make a huge difference in these patients' outcomes.

“Face of a Giant Panda” and “Beating Wings” in a Young Male

Mutations in the gene coding for ATPase copper transporting beta polypeptide (ATP7B) cause Wilson's disease, located on chromosome 13. It has mainly hepatic and neurological presentations. Movement disorders are a characteristic finding in Wilson's disease, and “wing-beating tremors” are classical characteristics found on physical examination. We came across and managed a case of Wilson's disease with primarily neurological presentation with classical wing-beating tremors and “face of a giant panda” on radiology. As the patient had very typical findings and he also improved with the treatment, it will be beneficial to the clinicians in their daily practice to identify the disease seeing these clinical findings.