Abstract
This study aimed to investigate the application of positron emission tomography- (PET-) computed tomography (CT) image information data combined with serous cavity effusion based on clone selection artificial intelligence algorithm in the diagnosis of patients with malignant tumors. A total of 97 patients with PET-CT scanning and empirically confirmed as serous cavity effusion were retrospectively analyzed in this study. The clone selection artificial intelligence algorithm was applied to register the PET-CT images, and the patients were rolled into a benign effusion group and a malignant effusion group according to the benign and malignant conditions of the serous cavity effusion. Besides, the causes of patients from the two groups were analyzed, and there was a comparison of their physiological conditions. Subsequently, CT values of different KeV, lipid/water, water/iodine, and water/calcium concentrations were measured, and the differences of the above quantitative parameters between benign and malignant serous cavity effusion were compared, as well as the registration results of the clone algorithm. The results showed that the registration time and misalignment times of clonal selection algorithm (13.88, 0) were lower than those of genetic algorithm (18.72, 8). There were marked differences in CT values of 40–60 keV and 130–140 keV between the two groups. The concentrations of lipid/water, water/iodine, and water/calcium in basal substances of the malignant effusion group were obviously higher than the concentrations of the benign effusion group (P < 0.05). Benign and malignant effusions presented different manifestations in PET-CT, which was conducive to the further diagnosis of malignant tumors. Based on clone selection artificial intelligence algorithm, PET-CT could provide a new multiparameter method for the identification of benign and malignant serous cavity effusions and benign and malignant tumors.
Highlights
Serous cavity effusion (SCE) is a common symptom and sign in clinical work, which is divided into pleural effusion, ascites, and pericardial effusion [1, 2]
The registration based on the shape feature points of the positron emission tomography- (PET-)computed tomography (CT) image is difficult and requires the application of external reference point features for registration
Its core is to multiply replication operator and mutation operator, retain the best individual, and improve the poor individual. It is an efficient and fast convergence algorithm. erefore, the patients were detected by PET-CT using clone selection artificial intelligence algorithm in this study, and patients with benign and malignant effusions were evaluated, thereby providing new ideas for clinical treatment of peripheral facial paralysis and pointing out new directions
Summary
Serous cavity effusion (SCE) is a common symptom and sign in clinical work, which is divided into pleural effusion, ascites, and pericardial effusion [1, 2]. The traditional imaging detections such as X-ray, Journal of Healthcare Engineering ultrasound, and computed tomography (CT) can only detect diseases by relying on morphological changes, and it is impossible to achieve more effective inspections for early disease [7, 8] At this stage, medical diagnosis and treatment capabilities with the further advancement of E-health have been improved directly or indirectly from digital medical equipment to high-level information and knowledge sharing, and various digital imaging and postprocessing technologies make disease diagnosis more intuitive and accurate. Medical diagnosis and treatment capabilities with the further advancement of E-health have been improved directly or indirectly from digital medical equipment to high-level information and knowledge sharing, and various digital imaging and postprocessing technologies make disease diagnosis more intuitive and accurate Under this background, positron emission tomography(PET-) CT has gradually been applied in the detection of SCE. It is an efficient and fast convergence algorithm. erefore, the patients were detected by PET-CT using clone selection artificial intelligence algorithm in this study, and patients with benign and malignant effusions were evaluated, thereby providing new ideas for clinical treatment of peripheral facial paralysis and pointing out new directions
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