Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing

Diagnosis of Nonperitonealized Colorectal Cancer with Computerized Tomography Image Features under Deep Learning

This study aimed to explore the value of abdominal computerized tomography (CT) three-dimensional reconstruction using the dense residual single-axis super-resolution algorithm in the diagnosis of nonperitonealized colorectal cancer (CC). 103 patients with nonperitonealized CC (the lesion was located in the ascending colon or descending colon) were taken as the research subjects. The imagological tumor (T) staging, the extramural depth (EMD) of the cancer tissues, and the extramural vascular invasion (EMVI) grading were analyzed. A dense residual single-axis super-resolution network model was also constructed for enhancing CT images. It was found that the CT images processed using the algorithm were clear, and the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were 33.828 dB and 0.856, respectively. In the imagological T staging of CC patients, there were 17 cases in the T3 stage and 68 cases in the T4 stage. With the EMD increasing, the preoperative carcinoembryonic antigen (CEA) highly increased, and the difference was statistically significant (P < 0.05). The postoperative hospital stays of patients were also different with different grades of EMVI. The hospital stay of grade 1 patients (19.45 days) was much longer than that of grade 2 patients (13.19 days), grade 3 patients (15.36 days), and grade 4 patients (14.36 days); the differences were of statistical significance (P < 0.05). It was suggested that CT images under the deep learning algorithm had a high clinical value in the evaluation of T staging, EMD, and EMVI for the diagnosis of CC.

Effect Evaluation of Cardiac Resynchronization Therapy in Elderly Patients with Heart Failure by Ultrasound Image under QuickOpt Algorithm

This research was aimed at analyzing the application value of echocardiography and QuickOpt algorithm in optimizing parameters of cardiac resynchronization therapy (CRT) in elderly patients with heart failure. 50 elderly patients who were diagnosed with chronic heart failure and underwent CRT were chosen as the research objects. According to the different optimization methods, the patients were divided into the echocardiography group and QuickOpt algorithm group, 25 cases in each group. The general data, optimized intervals, corresponding maximum aortic velocity time integrals (aVTIs), cardiac ultrasound indicators, and ventricular arrhythmia episodes of the patients in the two groups were analyzed. The results showed that there was no significant difference in the optimized sensed atrioventricular (SAV), paced atrioventricular (PAV), and ventricle to ventricle (VV) intervals and the corresponding aVTIs obtained by echocardiography and QuickOpt (P > 0.05). The consistency analysis revealed that the aVTIs in the SAV, PAV, and VV intervals presented a good consistency (P < 0.01), which were obtained by the echocardiography and QuickOpt functional optimization; the concordance correlation coefficient (CCC) in them was 96.16%, 98.03%, and 95.48%, respectively. The left ventricular ejection fraction (LVEF) showed an increasing trend over time in both groups, while the left ventricular end systolic volume (LVESV), left ventricular end diastolic volume (LVEDV), and morphological right ventricle (MRV) showed the downward trends over time, and the differences between two groups were not significant (P > 0.05). For the premature ventricular contraction (PVC) of ventricular arrhythmia episodes, there was no significant difference between the two groups in log (PVCs) and log (PVC runs) (P > 0.05). It was also found that both echocardiography and QuickOpt algorithm could improve the cardiac function of patients with heart failure significantly and reduce ventricular arrhythmia episodes and ventricular remodeling via optimized CRT; there was no difference in the improvement effect of the two optimization methods. However, echocardiography was inferior to QuickOpt algorithm in terms of time-consuming optimization in the intervals. This provided a reference for the clinical diagnosis and treatment of elderly patients with heart failure.

Prognosis Analysis and Perioperative Research of Elderly Patients with Non-Muscle-Invasive Bladder Cancer under Computed Tomography Image of Three-Dimensional Reconstruction Algorithm

To analyze the application value of computed tomography (CT) based on a three-dimensional reconstruction algorithm in perioperative nursing research and prognosis analysis of non-muscle-invasive bladder cancer (NMIBC), a retrospective study was performed on 124 patients with NMIBC who underwent surgical treatment in the hospital. All patients underwent CT examination based on the three-dimensional reconstruction algorithm before surgery, and transurethral resection of the bladder tumor was performed. The patients receiving conventional care were classified as the control group, and those receiving comprehensive care were classified as the case group, and the recovery status and recurrence of the two groups were compared. The results showed that the accuracy, specificity, and sensitivity of CT imaging information based on the three-dimensional reconstruction algorithm for NMIBC patients were 89.38, 93.77, and 84.39, respectively. The incidence of bladder spasm (9.68%), bladder flushing time (1.56 d), and retention of drainage tube time (2.68 d) in the case group were obviously lower compared with the control group (30.65%, 2.32 d, and 5.19 d) ( $P$  < 0.05). Serum BLCA-1 (3.72 ng/mL) and CYFRA21-1 (5.68 μg/mL) in the case group were significantly lower than those in the control group, with a statistically considerable difference ( $P$  < 0.05). Compared with the control group, the scores of role function (89.82 points), emotional function (84.76 points), somatic function (79.23 points), and social function (73.93 points) in the case group were observably higher ( $P$  < 0.05). In addition, one year after the operation, CT examination showed that the recurrence rate in the case group (6.45%) was significantly lower than that in the control group (22.58%) ( $P$  < 0.05). Therefore, CT detection based on the three-dimensional reconstruction algorithm was particularly important for preoperative diagnosis, prognosis, and recurrence monitoring of NMIBC patients. It could provide great clinical value for the diagnosis and prognosis monitoring of NMIBC.

Analgesic Effects of Dexmedetomidine Combined with Spinal and Epidural Anesthesia Nursing on Prostate Hyperplasia Patients after Transurethral Resection of Prostate by Intelligent Algorithm-Based Magnetic Resonance Imaging

To analyze the investigation of the application effects of different doses of dexmedetomidine (Dex) with combined spinal and epidural anesthesia nursing on analgesia after transurethral resection of prostate (TURP) by intelligent algorithm-based magnetic resonance imaging (MRI), MRI imaging segmentation model of mask regions with convolutional neural network (Mask R-CNN) features was proposed in the research. Besides, the segmentation effects of Mask R-CNN, U-net, and V-net algorithms were compared and analyzed. Meanwhile, a total of 184 patients receiving TURP were selected as the research objects, and they were divided into A, B, C, and D groups based on random number table method, each group including 46 cases. Patients in each group were offered different doses of Dex, and visual analogue scale (VAS) and Ramsay scores of different follow-up visit time, use of other analgesics, the incidence of postoperative cystospasm, and nursing satisfaction of patients in four groups were compared. The results demonstrated that Dice similarity coefficient (DSC) value, specificity, and positive predictive value of Mask R-CNN algorithm were $0.623\pm 0.084$ , 98.61%, and 69.57%, respectively, all of which were higher than those of U-net and V-net algorithms. Pain VAS scores and the incidence of cystospasm at different time periods of groups B and C were both significantly lower than those of group D ( $P<0.05$ ). Ramsay scores of groups B and C at 8 hours, 12 hours, 24 hours, and 48 hours after the operation were all remarkably higher than those in group D ( $P<0.05$ ). Besides, nursing satisfaction of groups B and C was obviously superior to that in group D, and the difference demonstrated statistical meaning ( $P<0.05$ ). The differences revealed that Dex showed excellent analgesic and sedative effects and could effectively reduce the incidence of complications after TURP, including cystospasm and nausea. In addition, it helped improve nursing satisfaction and patient prognosis.

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm

The purpose of the research was to discuss the application values of deep learning algorithm-based computed tomography perfusion (CTP) imaging combined with head and neck computed tomography angiography (CTA) in the diagnosis of ultra-early acute ischemic stroke. Firstly, 88 patients with acute ischemic stroke were selected as the research objects and performed with cerebral CTP and CTA examinations. In order to improve the effect of image diagnosis, a new deconvolution network model AD-CNNnet based on deep learning was proposed and used in patient CTP image evaluation. The results showed that the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) of the AD-CNNnet method were significantly higher than those of traditional methods, while the normalized mean square error (NMSE) was significantly lower than that of traditional algorithms (P < 0.05). 80 cases were positive by CTP-CTA, including 16 cases of hyperacute ischemic stroke and 64 cases of acute ischemic stroke. The diagnostic sensitivity was 93.66%, and the specificity was 96.18%. The cerebral blood flow (CBF), cerebral blood volume (CBV), and the mean transit time (MTT) in the infarcted area were significantly greater than those in the corresponding healthy side area, and the time to peak (TTP) was significantly less than that in the corresponding healthy side area (P < 0.05). The cerebral perfusion parameters CBF, TTP, and MTT in the penumbra were significantly different from those in the infarct central area and the corresponding contralateral area, and TTP was the most sensitive (P < 0.05). To sum up, deep learning algorithm-based CTP combined with CTA could find the location of cerebral infarction lesions as early as possible to provide a reliable diagnostic result for the diagnosis of ultra-early acute ischemic stroke.

Efficacy of Morphine Combined with Mechanical Ventilation in the Treatment of Heart Failure with Cardiac Magnetic Resonance Imaging under Artificial Intelligence Algorithms

This study was aimed at exploring the efficacy of morphine combined with mechanical ventilation in the treatment of heart failure with artificial intelligence algorithms. The cardiac magnetic resonance imaging (MRI) under the watershed segmentation algorithm was proposed, and the local grayscale clustering watershed (LGCW) model was designed in this study. A total of 136 patients with acute left heart failure were taken as the research objects and randomly divided into the control group (conventional treatment) and the experimental group (morphine combined with mechanical ventilation), with 68 cases in each group. The left ventricular end-diastolic diameter (LVEDD), left ventricular end-systolic diameter (LVESD), left ventricular ejection fraction (LVEF), N-terminal pro-brain natriuretic peptide (NT-proBNP), arterial partial pressure of oxygen (PaO₂), and arterial partial pressure of carbon dioxide (PaCO₂) were observed. The results showed that the mean absolute deviation (MAD) and maximum mean absolute deviation (max-MAD) of the LGCW model were lower than those of the fuzzy k-nearest neighbor (FKNN) algorithm and local gray-scale clustering model (LGSCm). The Dice metric was also significantly higher than that of other algorithms with statistically significant differences (P < 0.05). After treatment, LVEDD, LVESD, and NT-proBNP of patients in the experimental group were significantly lower than those in the control group, and LVEF in the experimental group was higher than that in the control group (P < 0.05). PaO₂ of patients in the experimental group was also significantly higher than that in the control group (P < 0.05). It suggested that the LGCW model had a better segmentation effect, and morphine combined with mechanical ventilation gave a better clinical efficacy in the treatment of acute left heart failure, improving the patients' cardiac function and arterial blood gas effectively.

Image Enhancement Algorithm-Based Ultrasound on Pelvic Floor Rehabilitation Training in Preventing Postpartum Female Pelvic Floor Dysfunction

In order to explore the application value of image enhancement algorithm in evaluating pelvic floor rehabilitation training in the prevention of postpartum female pelvic floor dysfunction (FPFD), 70 patients with FPFD were selected as the study subjects and randomly divided into two groups. One group received routine nursing (control group, n = 35), and the other group received pelvic floor rehabilitation training based on routine nursing (experimental group, n = 35). In ultrasound images based on an image enhancement algorithm, the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), and Pelvic Floor Distress Inventory-20 (PFDI-20) were used to evaluate the efficacy. The results showed that after image enhancement algorithm processing, the image signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) of ultrasound images of patients with FPFD were significantly improved (P < 0.05); the mean square error (MSE) was significantly decreased (P < 0.05); the diagnostic accuracy of FPFD in the original ultrasound images was 73.34%, and that after image enhancement algorithm processing was significantly improved to be 89.86% (P < 0.05). In addition, the overall clinical response rate of FPFD in the experimental group (82.86%) was obviously higher than that in the control group (51.43%) (P < 0.05). After rehabilitation training, the ICIQ-SF and PFDI-20 scores of patients with FPFD in the two groups suggested a significant decrease (P < 0.05). In summary, using an image enhancement algorithm has a good application prospect in evaluating pelvic floor rehabilitation training in preventing postpartum FPFD and is worthy of further promotion.

Artificial Intelligence Algorithm-Based Computerized Tomography Image Features Combined with Serum Tumor Markers for Diagnosis of Pancreatic Cancer

The objective of this study was to analyze the value of artificial intelligence algorithm-based computerized tomography (CT) image combined with serum tumor markers for diagnoses of pancreatic cancer. In the study, 68 hospitalized patients with pancreatic cancer were selected as the experimental group, and 68 hospitalized patients with chronic pancreatitis were selected as the control group, all underwent CT imaging. An image segmentation algorithm on account of two-dimensional (2D)-three-dimensional (3D) convolution neural network (CNN) was proposed. It also introduced full convolutional network (FCN) and UNet network algorithm. The diagnostic performance of CT, serum carbohydrate antigen-50 (CA-50), serum carbohydrate antigen-199 (CA-199), serum carbohydrate antigen-242 (CA-242), combined detection of tumor markers, and CT-combined tumor marker testing (CT-STUM) for pancreatic cancer were compared and analyzed. The results showed that the average Dice coefficient of 2D-3D training was 84.27%, which was higher than that of 2D and 3D CNNs. During the test, the maximum and average Dice coefficient of the 2D-3D CNN algorithm was 90.75% and 84.32%, respectively, which were higher than the other two algorithms, and the differences were statistically significant (P < 0.05). The penetration ratio of pancreatic duct in the experimental group was lower than that in the control group, the rest were higher than that in the control group, and the differences were statistically significant (P < 0.05). CA-50, CA-199, and CA-242 in the experimental group were 141.72 U/mL, 1548.24 U/mL, and 83.65 U/mL, respectively, which were higher than those in the control group, and the differences were statistically significant (P < 0.05). The sensitivity, specificity, positive predictive value, and authenticity of combined detection of serum tumor markers were higher than those of CA-50, CA-199, and CA-242, and the differences were statistically significant (P < 0.05). The results showed that the proposed algorithm 2D-3D CNN had good stability and image segmentation performance. CT-STUM had high sensitivity and specificity in diagnoses of pancreatic cancer.