The study was intended to introduce a novel method for aided diagnosis of cardiovascular diseases based on photoplethysmography (PPG). For this purpose, 40 volunteers were recruited in this study, of whom the physiological and pathological information was collected, including blood pressure and simultaneous PPG data on fingertips, by using a sphygmomanometer and a smart fingertip sensor. According to the PPG signal and its first and second derivatives, 52 features were defined and acquired. The Relief feature selection algorithm was performed to calculate the contribution of each feature to cardiovascular diseases. And then 10 core features which had the greatest contribution were selected as an optimal feature subset. Finally, the efficiency of the Relief feature selection algorithm was demonstrated by the results of k-nearest neighbor (kNN) and support vector machine (SVM) classifier applications of the features. The prediction accuracy of kNN model and SVM reached 66.67% and 83.33% respectively, indicating that: ① Age was the foremost feature for aided diagnosis of cardiovascular diseases; ② The optimal feature subset provided an important evaluation of cardiovascular health status. The obtained results showed that the application of the Relief feature selection algorithm provided high accuracy in aided diagnosis of cardiovascular diseases.
Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.
In this paper, we present a 3D virtual phantom design software, which was developed based on object-oriented programming methodology and dedicated to medical physics research. This software was named Magical Phantom (MPhantom), which is composed of 3D visual builder module and virtual CT scanner. The users can conveniently construct any complex 3D phantom, and then export the phantom as DICOM 3.0 CT images. MPhantom is a user-friendly and powerful software for 3D phantom configuration, and has passed the real scene's application test. MPhantom will accelerate the Monte Carlo simulation for dose calculation in radiation therapy and X ray imaging reconstruction algorithm research.
ObjectiveTo introduce and interpret ABCD classification system for subaxial cervical spine injury.
MethodsThe literature related to subaxial cervical spine injury classification system was extensively reviewed, analyzed, and summarized so as to introduce the ABCD classification system.
ResultsThe ABCD classification system for subaxial cervical spine injury consists of 3 parts. The first part of the proposed classification is an anatomical descri ption of the injury; it del ivers the information whether injury is bony, ligamentous, or a combined one. The second part is the classification of nerve function, spinal stenosis, and spinal instabil ity. The last part is optional and denotes radiological examination which is used to define injury type. Several letters have been used for simplicity to del iver the largest amount of information. And a treatment algorithm based on the proposed classification is suggested.
ConclusionThe ABCD classification system is proposed for simplification. However further evaluation of this classification is needed.
As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.
Stress distribution of denture is an important criterion to evaluate the reasonableness of technological parameters, and the bite force derived from the antagonist is the critical load condition for the calculation of stress distribution. In order to improve the accuracy of stress distribution as much as possible, all-ceramic crown of the mandibular first molar with centric occlusion was taken as the research object, and a bite force loading method reflecting the actual occlusal situation was adopted. Firstly, raster scanning and three dimensional reconstruction of the occlusal surface of molars in the standard dental model were carried out. Meanwhile, the surface modeling of the bonding surface was carried out according to the preparation process. Secondly, the parametric occlusal analysis program was developed with the help of OFA function library, and the genetic algorithm was used to optimize the mandibular centric position. Finally, both the optimized case of the mesh model based on the results of occlusal optimization and the referenced case according to the cusp-fossa contact characteristics were designed. The stress distribution was analyzed and compared by using Abaqus software. The results showed that the genetic algorithm was suitable for solving the occlusal optimization problem. Compared with the reference case, the optimized case had smaller maximum stress and more uniform stress distribution characteristics. The proposed method further improves the stress accuracy of the prosthesis in the finite element model. Also, it provides a new idea for stress analysis of other joints in human body.
Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10?3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10?2. The R2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.
A new cool-tip radiofrequency (RF) ablation therapeutic instrument based on impedance control algorithm is introduced in this paper. The equipment is composed of hardware system and software system. The RF power output and real time data acquisition are completed by the hardware system, while the software is used mainly to finish the control of the ablation range, the core of which is impedance control algorithm, and it also used to complete the display of the real time data in the course of the experiment. The impedance algorithm has solved the problem of impedance increased rapidly during the RF ablation, which has also expanded the scope of ablation. The pig liver experiments showed that the impedance control algorithm had strong adaptability. It also obtained a result of ablation range up to 3.5~4.5 cm single needle. It has the high clinical practical value of one-time inactivation of 3~5 cm tumor.
Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.
ObjectiveTo construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction using deep learning algorithms. MethodsA retrospective analysis was collected breast ultrasound images of 178 patients with thyroid dysfunction from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to February 2024, which served as the training set. The deep learning algorithm was used to construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction. In addition, a retrospective analysis was collected breast ultrasound images of 81 patients with thyroid dysfunction from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from March 2024 to January 2025, which served as the validation set. The above system was used as validation set to diagnose whether patients with thyroid dysfunction had breast nodules, and the diagnostic efficacy of imaging physicians’ diagnosis and the intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction was analyzed. The consistency between the diagnosis of ultrasound physicians, intelligent ultrasound diagnosis system and the “gold standard” was tested by Kappa test. ResultsThere was no statistically significant difference in age, type of thyroid dysfunction, disease duration, number of breast nodules, and other clinical data between the training set and the validation set (P>0.05). The time required for the training set intelligent ultrasound diagnostic system to diagnose a single breast ultrasound image was (0.04±0.01) min, which was lower than that of an ultrasound specialist [(12.36±2.58) min], t=63.709, P＜0.001. The sensitivity, specificity, accuracy, and area under the curve (AUC) of detecting breast nodules in patients with thyroid dysfunction using an intelligent ultrasound diagnostic system were 97.87% (46/47), 100% (34/34), 98.77% (80/81), and 0.997 [95%CI: (0.951, 1.00)], respectively. The sensitivity, specificity, accuracy, and AUC of detecting breast nodules by ultrasound physicians were 89.36% (42/47), 91.18% (31/34), 90.12% (73/81), and 0.904 [95%CI: (0.818, 0.958)], respectively. The AUC of the intelligent ultrasound diagnosis system was higher than that of the ultrasound physician (Z=2.673, P=0.008). The detection results of breast nodules in patients with thyroid dysfunction diagnosed by ultrasound physicians were generally consistent with the “gold standard” (Kappa value=0.799, P＜0.001), while the intelligent ultrasound diagnosis system was in good agreement with the “gold standard” (Kappa value=0.975, P＜0.001). The confusion matrix results showed that the number of false positives was 3 and 0 for the ultrasound department physicians and the intelligent ultrasound diagnostic system, respectively, while the number of false negatives was 5 and 1. The calibration curve results indicated a high consistency between the diagnostic probability and the actual probability of the intelligent ultrasound diagnostic system, with the calibration curve fitting well with the ideal curve (Hosmer-Lemeshow test: χ2=1.246, P=0.997). ConclusionThe intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction constructed by deep learning algorithm has good diagnostic efficacy, which can help ultrasound physicians improve screening efficiency and accuracy.
