The mechanical behavior modeling of human soft biological tissues is a key issue for a large number of medical applications, such as surgery simulation, surgery planning, diagnosis, etc. To develop a biomechanical model of human soft tissues under large deformation for surgery simulation, the adaptive quasi-linear viscoelastic (AQLV) model was proposed and applied in human forearm soft tissues by indentation tests. An incremental ramp-and-hold test was carried out to calibrate the model parameters. To verify the predictive ability of the AQLV model, the incremental ramp-and-hold test, a single large amplitude ramp-and-hold test and a sinusoidal cyclic test at large strain amplitude were adopted in this study. Results showed that the AQLV model could predict the test results under the three kinds of load conditions. It is concluded that the AQLV model is feasible to describe the nonlinear viscoelastic properties of in vivo soft tissues under large deformation. It is promising that this model can be selected as one of the soft tissues models in the software design for surgery simulation or diagnosis.
Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: −0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
Image-guided radiation therapy using magnetic resonance imaging (MRI) is a new technology that has been widely studied and developed in recent years. The technology combines the advantages of MRI imaging, and can offer online real-time tracking of tumor and adjacent organs at risk, as well as real-time optimization of radiotherapy plan. In order to provide a comprehensive understanding of this technology, and to grasp the international development and trends in this field, this paper reviews and summarizes related researches, so as to make the researchers and clinical personnel in this field to understand recent status of this technology, and carry out corresponding researches. This paper summarizes the advantages of MRI and the research progress of MRI linear accelerator (MR-Linac), online guidance, adaptive optimization, and dosimetry-related research. Possible development direction of these technologies in the future is also discussed. It is expected that this review can provide a certain reference value for clinician and related researchers to understand the research progress in the field.
The paper introduces a training system for foot ulcer patients based on three axis accelerometer, which uses three axis accelerometer and Apple mobile phone platform to guide foot ulcer patients to carry out a variety of lower limb muscle tissues training. The acceleration values of three directions for the foot training is obtained by analog-to-digital conversion and transmitted to the Apple mobile phone via its Bluetooth low energy. The Apple mobile phone accomplishes acceleration data preprocessing, numerical filtering and adaptive dual-threshold processing by our developed application program, so as to achieve the purpose of foot gesture recognition. The experimental result shows that the design can effectively present the training situation and effect of patients, encourage patients to adhere to the training, and provide some reference data for doctors and patients.
Randomized controlled trial has been the "gold standard" for clinical trials, in which randomization serves as a fundamental principle of clinical trials and plays an important role in balancing covariates. The allocation probability in traditional design is fixed, while that in adaptive randomization can alter during the experiment according to the specified plan to achieve the purposes of balancing the sample size, maximizing the benefit of patient, or balancing covariates etc. In this study, the adaptive randomization methods applied in clinical trials are discussed to explore their advantages and disadvantages for providing reference for the randomization of clinical trials.
A new enhancement method is proposed based on the characteristics of fundus images in this paper. Firstly, top-hat transform is utilized to weaken the background. Secondly, contrast limited adaptive histogram equalization (CLAHE) is performed to improve the uneven illumination. Finally, two-dimensional matched filters are designed to further enhance the contrast between blood vessels and background. The algorithm was tested in DIARETDB0 databases and showed good applicability for both normal and pathological fundus images. A new no-reference image quality assessment method was used to evaluate the enhancement methods objectively. The results demonstrated that the proposed method could effectively weaken the background, increase contrast, enhance details in the fundus images and improve the image quality greatly.
Nowadays, for gait instability phenomenon, many researches have been carried out at home and abroad. However, the relationship between plantar pressure and gait parameters in the process of balance adjustment is still unclear. This study describes the human body adaptive balance reaction during slip events on slippery level walk by plantar pressure and gait analysis. Ten healthy male subjects walked on a level path wearing shoes with two contrastive contaminants (dry, oil). The study collected and analyzed the change rule of spatiotemporal parameters, plantar pressure parameters, vertical ground reaction force (VGRF), etc. The results showed that the human body adaptive balance reaction during slip events on slippery level walk mainly included lighter touch at the heel strikes, tighter grip at the toe offs, a lower velocity, a shorter stride length and longer support time. These changes are used to maintain or recover body balance. These results would be able to explore new ideas and provide reference value for slip injury prevention, walking rehabilitation training design, research and development of walking assistive equipments, etc.
Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.
Response-adaptive randomization (RAR) dynamically adjusts the probability of assigning patients to different groups, optimizing treatment efficacy and participant welfare. It is particularly suitable for clinical studies involving multiple interventions or dose-finding and seamless phase II/III trials. This paper systematically introduces the concept, principles, and types of RAR, as well as its application in clinical trials (including traditional Chinese medicine research). It also provides R implementation code, offering researchers practical tools aimed at promoting the adoption of RAR in clinical practice.
The covariate-adjusted response-adaptive randomisation (CARA) design combines the advantages of response-adaptive randomisation and covariate-adaptive randomisation, and improves the efficiency and reliability of clinical trials by combining analytical results and covariates and dynamically adjusting the allocation of subsequent patients. This paper describes in detail several methods of CARA design and their example applications of various methods, including the dominant confidence method, the urn model, the generalized linear model, and the Atkinson model, and provides the corresponding R codes in anticipation of a wider application of the provided R codes in clinical trials.