Melanocytic lesions occur on the surface of the skin, in which the malignant type is melanoma with a high fatality rate, seriously endangering human health. The histopathological analysis is the gold standard for diagnosis of melanocytic lesions. In this study, a fully automated intelligent diagnosis method based on deep learning was proposed to classify the pathological whole slide images (WSI) of melanocytic lesions. Firstly, the color normalization based on CycleGAN neural network was performed on multi-center pathological WSI; Secondly, ResNet-152 neural network-based deep convolutional network prediction model was built using 745 WSI; Then, a decision fusion model was cascaded, which calculates the average prediction probability of each WSI; Finally, the diagnostic performance of the proposed method was verified by internal and external test sets containing 182 and 54 WSI, respectively. Experimental results showed that the overall diagnostic accuracy of the proposed method reached 94.12% in the internal test set and exceeded 90% in the external test set. Furthermore, the color normalization method adopted was superior to the traditional color statistics-based and staining separation-based methods in terms of structure preservation and artifact suppression. The results demonstrate that the proposed method can achieve high precision and strong robustness in pathological WSI classification of melanocytic lesions, which has the potential in promoting the clinical application of computer-aided pathological diagnosis.
Objective To explore the correlation between body mass index (BMI) and disease severity in patients with spinocerebellar ataxia type 3 (SCA3). Methods Patients who visited the Department of Neurology of the First Affiliated Hospital of Fujian Medical University with a confirmed diagnosis of SCA3 between July 2022 and August 2023 were selected as a case group, and healthy individuals between June 2024 and October 2024 were selected as a control group, and the BMI levels of the two groups were compared. Patient demographics and clinical statistics were collected, the severity of ataxia in SCA3 patients was assessed using the Scale for the Assessment and Rating of Ataxi, and the relationship between BMI and disease severity was evaluated. Results A total of 101 patients and 101 healthy individuals were included. The BMI levels of SCA3 patients were significantly lower than those of normal controls (t=−2.370, P=0.019). The results of the multiple linear regression model showed that the BMI, disease duration and smoking history had an effect on the disease severity of SCA3 patients (P<0.05), and disease duration and disease severity had a significant effect on the disease progression in SCA3 patients (P<0.05). Conclusion There may be a correlation between BMI and disease severity in SCA3 patients, and controlling the BMI level may help to control the disease in SCA3 patients.