Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.
Valvular heart disease (VHD) ranks as the third most prevalent cardiovascular disease, following coronary artery disease and hypertension. Severe cases can lead to ventricular hypertrophy or heart failure, highlighting the critical importance of early detection. In recent years, the application of deep learning techniques in the auxiliary diagnosis of VHD has made significant advancements, greatly improving detection accuracy. This review begins by introducing the etiology, pathological mechanisms, and impact of common valvular heart diseases. It then explores the advantages and limitations of using electrocardiographic signals, phonocardiographic signals, and multimodal data in VHD detection. A comparison is made between traditional risk prediction methods and large language models (LLMs) for predicting cardiovascular disease risk, emphasizing the potential of LLMs in risk prediction. Lastly, the current challenges faced by deep learning in this field are discussed, and future research directions are proposed.
ObjectiveTo conduct a Meta-analysis to determine the clinical effect of protocol biopsy (PB)-monitored therapy after renal transplantation.MethodsPubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang Standards Database and VIP Database for Chinese Technical Periodicals were searched for trials comparing the efficacy of timely intervention under PB surveillance with the conventional treatment. The quality of included studies was assessed and Meta-analysis was conducted by RevMan 5.3 software.ResultsSix randomized controlled trials met our inclusion criteria, including 698 cases. No significant difference was found between the PB group and the control group in 1-year [relative risk (RR)=0.99, 95% confidence interval (CI) (0.97, 1.01), P=0.39] and 2-year recipient survival rate [RR=1.00, 95%CI (0.97, 1.02), P=0.72]. Graft survival rate after 1 year [RR=1.01, 95%CI (0.99, 1.04), P=0.29] and 2 years [RR=1.02, 95%CI (0.99, 1.06), P=0.19] were also statistically similar. No statistical difference was found in glomerular filtration rate between the two groups [mean difference (MD)=0.45 mL/(min·1.73 m2), 95%CI (–3.77, 4.67) mL/(min·1.73 m2), P=0.83]. Renal function of PB group, monitored by serum creatinine, was superior to the control group [MD=–0.46 mg/dL, 95%CI (–0.63, –0.29) mg/dL, P<0.000 01]. No statistical difference was found in infection between the two groups [RR=1.23, 95%CI (0.69, 2.19), P=0.48].ConclusionsOur study did not suggest PB for every kidney transplantation recipient. However, long-term randomized controlled trials with larger sample size would be necessary to determine whether PB was effective for specific populations.