ObjectiveTo develop an artificial intelligence based three-dimensional (3D) preoperative planning system (AIHIP) for total hip arthroplasty (THA) and verify its accuracy by preliminary clinical application.MethodsThe CT image database consisting of manually segmented CT image series was built up to train the independently developed deep learning neural network. The deep learning neural network and preoperative planning module were assembled within a visual interactive interface—AIHIP. After that, 60 patients (60 hips) with unilateral primary THA between March 2017 and May 2020 were enrolled and divided into two groups. The AIHIP system was applied in the trial group (n=30) and the traditional acetate templating was applied in the control group (n=30). There was no significant difference in age, gender, operative side, and Association Research Circulation Osseous (ARCO) grading between the two groups (P>0.05). The coincidence rate, preoperative and postoperative leg length discrepancy, the difference of bilateral femoral offsets, the difference of bilateral combined offsets of two groups were compared to evaluate the accuracy and efficiency of the AIHIP system.ResultsThe preoperative plan by the AIHIP system was completely realized in 27 patients (90.0%) of the trial group and the acetate templating was completely realized in 17 patients (56.7%) of the control group for the cup, showing significant difference (P<0.05). The preoperative plan by the AIHIP system was completely realized in 25 patients (83.3%) of the trial group and the acetate templating was completely realized in 16 patients (53.3%) of the control group for the stem, showing significant difference (P<0.05). There was no significant difference in the difference of bilateral femoral offsets, the difference of bilateral combined offsets, and the leg length discrepancy between the two groups before operation (P>0.05). The difference of bilateral combined offsets at immediate after operation was significantly less in the trial group than in the control group (t=?2.070, P=0.044); but there was no significant difference in the difference of bilateral femoral offsets and the leg length discrepancy between the two groups (P>0.05).ConclusionCompared with the traditional 2D preoperative plan, the 3D preoperative plan by the AIHIP system is more accurate and detailed, especially in demonstrating the actual anatomical structures. In this study, the working flow of this artificial intelligent preoperative system was illustrated for the first time and preliminarily applied in THA. However, its potential clinical value needs to be discovered by advanced research.
Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA) theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.
Stage ⅠA lung adenocarcinoma presented as ground glass dominant on thin-section high-resolution CT scan is a special subtype of lung cancer. The characteristics of this subtype are quite different from the other patients, which presented as lower malignancy and better prognosis. Clinical, pathological and imaging studies have revealed that the proportion of the solid component in part-solid ground glass nodule is closely related with the pathological type and the prognosis of lung cancer. The methods for the assessment of the solid components in the ground glass nodule can be classified into three types, including subjective assessment, two dimensional measurement and three dimensional measurement. This review summarized the advantages and the limitations of these three methods. We also reviewed the clinical application of these techniques.
Traditional methods of non-contact human respiratory rate measurement usually require complex devices or algorithms. Aiming at this problem, a non-contact respiratory rate measurement method based on only the RGB video information was proposed in this paper. The method consisted of four steps. Firstly, spatial filtering was applied to each frame of the input video. Secondly, a gray compensation algorithm was used to compensate for the gray level change caused by the environmental light. Thirdly, the gray levels of each pixel over time were filtered separately by a low-pass filter. Finally, the region of interest was determined based on the filtering results, and the respiration rate of the human is measured. The physical measurement experiments were designed, and the measurement accuracy was compared with that of the biological radar. The error of the proposed method was between ? 5.5% and 3% in different detection directions. The results show that the non-contact respiration rate measurement method can effectively measure the human respiration rate.
Multi-task learning (MTL) has demonstrated significant advantages in the field of physiological signal measurement. This approach enhances the model's generalization ability by sharing parameters and features between similar tasks, even in data-scarce environments. However, traditional multi-task physiological signal measurement methods face challenges such as feature conflicts between tasks, task imbalance, and excessive model complexity, which limit their application in complex environments. To address these issues, this paper proposes an enhanced multi-scale spatiotemporal network (EMSTN) based on Eulerian video magnification (EVM), super-resolution reconstruction and convolutional multilayer perceptron. First, EVM is introduced in the input stage of the network to amplify subtle color and motion changes in the video, significantly improving the model's ability to capture pulse and respiratory signals. Additionally, a super-resolution reconstruction module is integrated into the network to enhance the image resolution, thereby improving detail capture and increasing the accuracy of facial action unit (AU) tasks. Then, convolutional multilayer perceptron is employed to replace traditional 2D convolutions, improving feature extraction efficiency and flexibility, which significantly boosts the performance of heart rate and respiratory rate measurements. Finally, comprehensive experiments on the Binghamton-Pittsburgh 4D Spontaneous Facial Expression Database (BP4D+) fully validate the effectiveness and superiority of the proposed method in multi-task physiological signal measurement.
To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [–4.78, 4.78] beats per minute, and a consistency error of –0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.
It is crucial to select outcomes in clinical trials. Appropriate outcomes can improve value and significance of trials and reduce the cost of investment. This paper describes how to develop core outcome sets and core outcome measurement instrument sets with the theory of mixed methods research, so as to standardize the choice of outcomes and outcome measurement instruments in clinical trials.
ObjectiveTo three-dimensionally calculate the craniofacial parameters of midface of patients with Treacher Collins syndrome (TCS) in China, in order to understand the changes in the spatial position relationship between the various anatomical structures of the midface.MethodsCT imaging data of TCS patients and age- and gender-matched normal populations between January 2013 and July 2020 was retrospectively analyzed. A total of 33 cases met the selection criteria for inclusion in the study, including 14 cases in the TCS group and 19 cases in the control group. ProPlan CMF 3.0 software was used to perform three-dimensional digital reconstruction of the craniofacial bone, measure the anatomical parameters of the midface, and analyze its morphological structure; at the same time perform three-dimensional digital reconstruction of the upper airway for morphological analysis (measure upper airway volume).ResultsCT images analysis revealed that all 14 patients with TCS presented the typical features with downward slanting of the palpebral fissures and different degrees of zygomatico-orbital complex dysplasia. Cephalometric and morphological analysis of the midface revealed that, multiple transverse diameters of the midface of TCS patients were significantly decreased when compared with the control group (P<0.05), such as the width of the maxillary base, the length of the maxillary complex, and some distances related to the nasal morphology; but the distance between bilateral orbitales increased in TCS group (P<0.05). Several anteroposterior distances in TCS group were decreased significantly when compared to control group and the distance between the skull base point and the posterior nasal spine was the most shortened (P<0.05). But there was no significant difference of the distance between nasion and anterior nasal spine, which represented anterior midface height, between groups (P>0.05). The skull base angle and SNB angle (the angle between the sella point-nose root point-inferior alveolar seat point) of the TCS group both decreased when compared with the control group (P<0.05), but there was no significant difference in SNA angle (the angle between the sella point-nose root point-upper alveolar seat point) between the two groups (P>0.05). The total volume of the upper airway was (24 621.07±8 476.63) mm3 in the TCS group, which was significantly lower than that of the control group [(32 864.21±13 148.74) mm3] (t=2.185, P=0.037).ConclusionThe transverse distances, anteroposterior distances, and multiple craniofacial angles measurement of TCS patients were significantly decreased when compared to the control group, presented with different degrees of zygomatico-orbital complex dysplasia, nasal and maxillary dysplasia, but there was no obvious restriction in face height development. Reduced internal diameters of the upper airway maybe responsible for the decreased upper airway volume of patients with TCS.
To address the challenges of capturing micro-strains in detecting esophageal motility disorders and the limitations of existing high-resolution manometry and functional intraluminal imaging probes in directly measuring esophageal tissue electrical impedance, this study proposes a novel flexible balloon sensor structure that integrates a piezoelectric film assembly with a distributed impedance electrode array. Using the electrical analysis module in the finite element analysis (FEA) software, simulations of the forward problem for esophageal impedance detection were conducted to optimize the excitation source parameters, and a physical prototype was fabricated. Under a relative excitation mode with a voltage sensitivity of 2.059%, the voltage output characteristics of the impedance electrode array were analyzed during linear changes in the balloon filling volume. Based on the performance variation of the piezoelectric film assembly, 80% was selected as the optimal filling volume. Force-electric coupling tests were conducted on the balloon sensor using a pressure testing platform, revealing that both the piezoelectric film assembly inside the balloon and the impedance electrodes outside the balloon exhibited significant load differentiation characteristics as the force application point shifted. In summary, this balloon sensor facilitates the localization of force application while simultaneously analyzing esophageal tissue properties, offering a novel diagnostic approach and objective tool for esophageal disease detection.
Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
