Technology and Health Care

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ISSN / EISSN : 0928-7329 / 1878-7401
Published by: IOS Press (10.3233)
Total articles ≅ 2,125
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Carolin Melcher
Published: 16 June 2022
Technology and Health Care, pp 1-12; https://doi.org/10.3233/thc-223389

Abstract:
BACKGROUND: Surgical decompression is the intervention of choice for lumbar spinal stenosis (LSS) when non-operative treatment has failed. Apart from acute complications such as hematoma and infections, same-level recurrent lumbar stenosis and adjacent-segment disease (ASD) are factors that can occur after index lumbar spine surgery. OBJECTIVE: The aim of this retrospective case series was to evaluate the outcome of surgery and the odds of necessary revisions. METHODS: Patients who had undergone either decompressive lumbar laminotomy or laminotomy and spinal fusion due to lumbar spinal stenosis (LSS) between 2000 and 2011 were included in this analysis. Demographic, perioperative and radiographic data were collected. Clinical outcome was evaluated using numeric rating scale (NRS), the symptom subscale of the adapted version of the german Spinal Stenosis Measure (SSM) and patient-sreported ability to walk. RESULTS: Within the LSS- cohort of 438 patients, 338 patients underwent decompression surgery only, while instrumentation in addition to decompression was performed in 100 cases (22.3%). 38 patients had prior spinal operations (decompression, disc herniation, fusion) either at our hospital or elsewhere. Thirty-five intraoperative complications were documented with dural tear with CSF leak being the most common (33/35; 94.3%). Postoperative complications were defined as complications that needed surgery and differentiated between immediate postoperative complications (⩽ 3 weeks post operation) and complications that needed revisions surgery at a later date. Within all patients 51 revisions were classified as immediate complications of the index operation with infections, neurological deficits and hematoma being the most common. Within this group only 22 patients had fusion surgery in the first place, while 29 were treated by decompression. Revision surgery was indicated by 53 patients at a later date. While 4 patients decided against surgery, 49 revision surgeries were planned. 28 were performed at the same level, 10 at the same level plus an adjacent level, and 10 were executed at index level with indications of adjacent level spinal stenosis, adjacent level spinal stenosis plus instability and stand-alone instability. Pre- operative VAS score and ability to walk improved significantly in all patients. CONCLUSIONS: While looking for predictors of revision surgery due to re-stenosis, instability or same/adjacent segment disease none of these were found. Within our cohort no significant differences concerning demographic, peri-operative and radiographic data of patients with or without revision wer noted. Patients, who needed revision surgery were older but slightly healthier while more likely to be male and smoking. Surprisingly, significant differences were noted regarding the distribution of intraoperative and early postoperative complications among the 6 main surgeons while these weren’t obious within the intial index group of late revisions.
Lan Wang, Yanqi Wu, Min Zhu, Cuilian Zhao
Published: 16 June 2022
Technology and Health Care, pp 1-10; https://doi.org/10.3233/thc-213545

Abstract:
BACKGROUND: Lip incompetence resulting from mouth breathing is a common clinical manifestation, while there are no definite indicators of amplitude and intensity of muscle functional training in clinical practice, which leads to unsatisfactory training results. OBJECTIVE: The aim was to quantify the relationship between electromyography (EMG) and force in orbicularis oris muscle, so that the indicators of muscle functional training can be evaluated using EMG signals, so as to improve the training effects. METHODS: The EMG and the force signals of orbicularis oris muscle from 0% to 100% MVC within 5 s in twelve healthy subjects (six males and six females; age, 25 ± 2 years; mass, 60 ± 15 kg) were recorded simultaneously for three trials. Four EMG features consisting of RMS, WAMP, SampEn and FuzzyEn were analyzed. The regression analyses were performed using first-order and third-order polynomial model. RESULTS: There were high correlations between the four EMG features and muscle force with the two models. The third-order model yielded a higher coefficient of determination (R2) than the linear model (p< 0.001) and the result of FuzzyEn (R2: 0.884 ± 0.059) was the highest in the four features. CONCLUSION: The third-order model with FuzzyEn of EMG signals may be used to guide the muscle functional training.
Xinwei Guo, Hongyan Zhao, Zhimin Zhang
Published: 16 June 2022
Technology and Health Care, pp 1-15; https://doi.org/10.3233/thc-220117

Abstract:
BACKGROUND: The similar elastic modulus of resin-matrix ceramics to dentin has resulted in their recent widespread application clinically. Nevertheless, the bacterial environment of oral cavity can degrade the resin composite. OBJECTIVE: The objective was to analyse the effect of S. mutans and its fluoride-resistant strains on the adhesion of three CAD/CAM ceramics. METHODS: S. mutans UA159 (UA) was identified, and its fluoride-resistant strain (FR) was induced. For crack observation, three kinds of CAD/CAM ceramics (IPS Empress, Lava Ultimate and Vita Enamic) were bonded with tooth complex (enamel, dentin and flowable resin) through adhesive. For micro-tensile test, ceramics were bonded with flowable resin, and cut into strip test pieces. Then specimens were immersed into the UA, FR and the control solution (BHI) separately for 14 d. Ceramic-adhesive interface and adhesive-tooth complex interface were observed and analyzed through electron microscope and stereomicroscope. Micro-tensile test was conducted. RESULTS: Specimens in bacterial solutions had more cracks and comparatively weaker micro-tensile strength than those in BHI. In ceramic-adhesive interface, Lava Ultimate produced the most cracks. In adhesive-tooth complex interface, adhesive-dentin produced the most cracks. Meanwhile, IPS Empress had the strongest micro-tensile strength when bonded with resin. CONCLUSIONS: S. mutans and its fluoride resistant strain can cause cracks in the bonding of ceramics and dental tissue, especially resin-matrix ceramic and dentin, and weaken the bonding strength between ceramics and resin.
Siqi He, Bo Xiao, Huajiang Wei, Shenjiao Huang, Tongsheng Chen
Published: 16 June 2022
Technology and Health Care, pp 1-12; https://doi.org/10.3233/thc-220031

Abstract:
BACKGROUND: Cervical histopathology image classification is a crucial indicator in cervical biopsy results. OBJECTIVE: The objective of this study is to identify histopathology images of cervical cancer at an early stage by extracting texture and morphological features for the Support Vector Machine (SVM) classifier. METHODS: We extract three different texture features and one morphological feature of cervical histopathology images: first-order histogram, K-means clustering, Gray Level Co-occurrence Matrix (GLCM) and nucleus feature. The original dataset used in our experiment is obtained from 20 patients diagnosed with cervical cancer, including 135 whole slide images (WSIs). Given an entire WSI, the patches on its tissue region are extracted randomly. RESULTS: We finally obtain 3,000 patches, including 1,000 normal, 1,000 hysteromyoma and 1,000 cancer images. Among them, 80% of the entire data set is randomly selected as training set and the remaining 20% as test set. The accuracy of SVM classification using first-order histogram, K-means clustering, GLAM and nucleus feature for extracting features are respectively 87.4%, 90.6%, 91.6% and 93.5%. CONCLUSIONS: The classification accuracy of the SVM combining the four features is 96.8%, and the proposed nucleus feature plays a key role in the SVM classification of cervical histopathology images.
Wanxiang Wang, Yong He, Feng Li, Jinke Li, Jingshuai Liu, Xinyu Wu
Published: 16 June 2022
Technology and Health Care, pp 1-13; https://doi.org/10.3233/thc-220087

Abstract:
BACKGROUND: The digital twin concept is the virtual model based on entity design measures, which is used in many enterprises’ virtual workshop design models for workshop production scheduling and optimization. However, in the field of medical rehabilitation, the integration of digital twin technology started late compared to traditional industrial manufacturing. Many current digital models are not well suited for information interaction between patients and devices. OBJECTIVE: In order to address the lack of interaction between patients and devices in the field of medical rehabilitation, this paper proposes an automatic gait data control system (AGDCS) for fully actuated lower limb exoskeleton digital twinning. This system improves the integration of digital twinning system with the medical rehabilitation field and analyzes the patient’s gait data through simulation experiments. METHODS: The digital twin system was designed in several steps. Firstly, the upper computer function module was designed and developed according to the rehabilitation treatment needs. After that, the combination of exoskeleton robot and software was carried out, and finally the real rehabilitation treatment environment of patients was simulated through experiments. RESULTS: The proposed system was very reliable in the experimental tests of the host computer and exoskeleton robot. In the upper computer test, the patient specific gait can be generated, and the motion of the exoskeleton robot can be observed in real-time. During the walking test of the exoskeleton robot, the exoskeleton robot completed the specified gait. The result verified the superiority and effectiveness of the digital twin system AGDCS in the field of rehabilitation. CONCLUSIONS: The digital twin system proposed in this paper improves the interaction between self-balancing exoskeleton robot and patients, and improves the autonomy and safety of patients in rehabilitation treatment.
Yongtao Wang, Shengwei Tian, Long Yu, Weidong Wu, Dezhi Zhang, Junwen Wang, Junlong Cheng
Published: 16 June 2022
Technology and Health Care, pp 1-15; https://doi.org/10.3233/thc-220174

Abstract:
BACKGROUND: The results of medical image segmentation can provide reliable evidence for clinical diagnosis and treatment. The U-Net proposed previously has been widely used in the field of medical image segmentation. Its encoder extracts semantic features of different scales at different stages, but does not carry out special processing for semantic features of each scale. OBJECTIVE: To improve the feature expression ability and segmentation performance of U-Net, we proposed a feature supplement and optimization U-Net (FSOU-Net). METHODS: First, we put forward the view that semantic features of different scales should be treated differently. Based on this view, we classify the semantic features automatically extracted by encoders into two categories: shallow semantic features and deep semantic features. Then, we propose the shallow feature supplement module (SFSM), which obtains fine-grained semantic features through up-sampling to supplement the shallow semantic information. Finally, we propose the deep feature optimization module (DFOM), which uses the expansive convolution of different receptive fields to obtain multi-scale features and then performs multi-scale feature fusion to optimize the deep semantic information. RESULTS: The proposed model is experimented on three medical image segmentation public datasets, and the experimental results prove the correctness of the proposed idea. The segmentation performance of the model is higher than the advanced models for medical image segmentation. Compared with baseline network U-NET, the main index of Dice index is 0.75% higher on the RITE dataset, 2.3% higher on the Kvasir-SEG dataset, and 0.24% higher on the GlaS dataset. CONCLUSIONS: The proposed method can greatly improve the feature representation ability and segmentation performance of the model.
R.J. Seemann, A.M. Mielke, D.L. Glauert, T. Gehlen, A.S. Poncette, L.K. Mosch, D.A. Back
Published: 16 June 2022
Technology and Health Care, pp 1-8; https://doi.org/10.3233/thc-220138

Abstract:
BACKGROUND: Digital competencies are more and more required in everyday work, and training future healthcare professionals in digital health is highly important. OBJECTIVE: Aim of this study was to assess medical students’ gain of knowledge by participation in a teaching module “Digital Health”, and to evaluate their attitudes towards digital health and its role in medical education. METHODS: Students of the module were asked to complete a questionnaire and a multiple-choice-test before and after completing the classes. Students of the same educational level in different modules served as reference group. RESULTS: 34 students took part (n= 17 “Digital Health group”; n= 17 “reference group”). There was no significant difference in pre-existing knowledge between the groups. After having completed the module, participants reached significantly higher scores, compared to their preexisting knowledge (p< 0.05) and the reference group (p< 0.05). Most students found that digital medicine is not sufficiently represented in undergraduate medical education, but will influence everyday work of physicians in the next five years. CONCLUSIONS: Students showed a high awareness for the impact of digital health on physicians’ work. The results suggest that the format can sufficiently transfer knowledge about digital health. Teaching of digital knowledge and competencies should be firmly implemented into medical education to form digitally competent future doctors.
Ping Shi, Kaixin Fang, Hongliu Yu
Published: 10 June 2022
Technology and Health Care, pp 1-15; https://doi.org/10.3233/thc-213320

Abstract:
BACKGROUND: At present, the popular control method for intelligent bionic prosthetic hands is EMG control. However, the control accuracy of this method is low. It is a trend to integrate computer vision into the prosthetic hand. OBJECTIVE: The purpose of this paper is to design an intelligent prosthetic hand based on image recognition, improve the control accuracy and the quality of life of the disabled. METHODS: Convolutional neural network is used to recognize the object to be grasped, and the recognition result is used as a trigger signal to control our intelligent prosthetic hand. We have designed a four-bar linkage mechanism and a side swing mechanism in the structure, which can not only achieve the flexion and extension of fingers but also realize the adduction and abduction of the four fingers and the lateral swing of the thumb. RESULTS: Through the method of image recognition, the new intelligent bionic hand can achieve five kinds of Human action. Including grasp, side pinch, three-finger pinch, two-finger pinch, and pinch between fingers. CONCLUSIONS: The experiment result proves that the precision of image recognition control is very excellent, the intelligent prosthetic hand can be completed the corresponding task.
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