Abstract
Objective. The aim of this study was to explore the application value of double X-ray image based on neural network in comparison of the efficacy and safety of deep-water proteolytic milk powder and parenteral nutrition in the intervention of neonatal noninfectious abdominal distention. Methods. Clinical data of 58 neonates diagnosed with noninfectious abdominal distention were retrospectively analyzed. 2D-3D registration was simplified into two steps by decomposing spatial rigid-body transformation parameters into two planes, including 2D-2D approximate rigid-body registration and single-parameter 2D-3D rigid-body registration. Then, the convolution neural network was used to fit the nonlinear mapping relationship between the residual of X-ray images and the corresponding attitude differences of children, and the residual regression spatial rigid-body transformation parameters of the X-DRR image pairs were obtained. Noninfectious abdominal distention was diagnosed in all neonates, of which 28 neonates were treated with deep hydrolyzed protein milk powder. Another 30 neonates who received parenteral nutrition support were set as control group. All newborns received two weeks of treatment. The total effective rate, birth weight recovery, weight growth rate, intestinal feeding recovery time, and incidence of feeding intolerance were compared between the two groups. Results. Spatial coordinate decomposition using double X-ray can simplify the mapping relationship between spatial coordinate transformation and X-DRR residual image. Compared with the gray level iterative optimization registration algorithm, the registration accuracy and speed were significantly improved. The total effective rate in the treatment group (92.86%) was significantly higher than that in the control group (9%). The recovery time of birth weight, intestinal feeding recovery time, and meconium excretion time were significantly shorter than those in the control group, and the body weight in the treatment group increased faster than that in the control group ( P < 0.05 ). In addition, the incidence of feeding intolerance was 3.57% (1/28) in the treatment group and 36.36% (8/22) in the control group, which was significantly lower than that in the treatment group ( P < 0.05 ). Conclusion. After data training, the network can complete accurate double ray registration in 0.04 s. Deep hydrolyzed protein milk powder had remarkable therapeutic effect on neonates, with no infective abdominal distention, fast recovery, and low incidence of feeding intolerance, which was safe and reliable in clinical application.
Highlights
Abdominal distention is a common disease of newborns
Scientific Programming abdominal distention is mild, usually caused by stress reactions caused by other serious diseases or by the inhalation of a large amount of air by the newborn
An iteratively optimized medical image registration algorithm was proposed to improve the registration speed of medical images by applying deep hydrolyzed protein milk powder and parenteral nutrition in the treatment of neonatal noninfectious abdominal distension. 2D-3D registration was simplified into two steps, including 2D-2D approximate rigid body registration and single-parameter 2D-3D rigid body registration. en, the convolution neural network was used to fit the nonlinear mapping relationship between the residual of the X-ray images of children and their corresponding attitude differences. e residual regression spatial rigid-body transformation parameters of the X-digitally reconstructed radiographic images (DRR) images were obtained, and fast 2D-3D registration was realized, providing reference for clinical diagnosis of abdominal distension in children
Summary
Abdominal distention is a common disease of newborns. It is often a complication of multiple diseases of newborns, which affects their breathing and heart rate, and often accompanies other serious diseases. erefore, it is necessary to diagnose the causes in time and make treatment as early as possible [1, 2]. Rough measurement, assessment, classification, diagnosis, and preoperative design, deep learning-based images can assist physicians to make early disease diagnosis, positive treatment plans, and effective clinical decisions. It can effectively improve the efficiency of medical imaging in disease detection, recognition, and diagnosis and promote and realize computer-assisted therapy in the medical and health field [12]. An iteratively optimized medical image registration algorithm was proposed to improve the registration speed of medical images by applying deep hydrolyzed protein milk powder and parenteral nutrition in the treatment of neonatal noninfectious abdominal distension. An iteratively optimized medical image registration algorithm was proposed to improve the registration speed of medical images by applying deep hydrolyzed protein milk powder and parenteral nutrition in the treatment of neonatal noninfectious abdominal distension. 2D-3D registration was simplified into two steps, including 2D-2D approximate rigid body registration and single-parameter 2D-3D rigid body registration. en, the convolution neural network was used to fit the nonlinear mapping relationship between the residual of the X-ray images of children and their corresponding attitude differences. e residual regression spatial rigid-body transformation parameters of the X-DRR images were obtained, and fast 2D-3D registration was realized, providing reference for clinical diagnosis of abdominal distension in children
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