Role of artificial intelligence and machine learning in haematology

Application of artificial intelligence in the diagnosis and treatment of urinary tumors

Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery.

Suction detection and suction suppression of centrifugal blood pump based on the FFT-GAPSO-LSTM model and speed modulation

Centrifugal blood pumps are important devices used to treat heart failure. However, they are prone to high-risk suction events that pose a threat to human health when operating at high speeds. To address these issues, a normal suction detection method and a suction suppression method based on the FFT-GAPSO-LSTM model and speed modulation were proposed. The innovation of this suction detection method lies in the application of the genetic particle swarm optimisation (GAPSO) and the fast Fourier transform (FFT) feature extraction method to the long-term and short-term memory (LSTM) model, thereby improving the accuracy of suction detection. After detecting signs of suction, the suction suppression method designed in this study based on variable-speed modulation immediately takes effect, enabling the centrifugal blood pump to quickly return to its normal state by controlling the speed. The suction detection method was divided into four steps. First, a mathematical model of the coupling of the cardiovascular system and the centrifugal blood pump was established, and a real-time blood flow curve was obtained through model simulation. Second, the signal was preprocessed by adding Gaussian white noise and low-pass filtering to make the blood flow signal close to actual working conditions while retaining the original characteristics. Subsequently, through fast Fourier transform (FFT) analysis of the processed curve, the spectral characteristics that can characterise the working state of the centrifugal blood pump were extracted. Finally, the parameters of the LSTM model were optimised using the GAPSO, and the improved LSTM model was used to train and test the blood flow spectrum feature set. The results show that the suction detection method of the FFT-GAPSO-LSTM model can effectively detect whether centrifugal blood pump suction occurs and has certain advantages over other methods. In addition, the simulation results of the suction suppression were excellent and could effectively suppress the occurrence of suction. These results provide a reference for the design of centrifugal blood pump control systems.