Technology and Health Care - Volume 32, issue S1

Authors: Li, Zongjin | Song, Changxin | Yang, Jiyu | Jia, Zeyu | Chen, Dongzhen | Yan, Chengying | Tian, Liqin | Wu, Xiaoming

Article Type: Research Article

Abstract: BACKGROUND: Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. OBJECTIVE: This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. METHODS: A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson’s or Spearman’s correlation. It was then used to construct a nearest-neighbor table …based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. RESULTS: Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. CONCLUSIONS: DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules. Show more

Keywords: Clustering, co-expression module, gene expression data, similarity measure

DOI: 10.3233/THC-248020

Citation: Technology and Health Care, vol. 32, no. S1, pp. 229-239, 2024

Authors: Dou, Yifeng | Liu, Jiantao | Meng, Wentao | Zhang, Yingchao

Article Type: Research Article

Abstract: BACKGROUND: With the advent of artificial intelligence technology, machine learning algorithms have been widely used in the area of disease prediction. OBJECTIVE: Cardiovascular disease (CVD) seriously jeopardizes human health worldwide, thereby needing the establishment of an effective CVD prediction model that can be of great significance for controlling the risk of the disease and safeguarding the physical and mental health of the population. METHODS: Considering the UCI heart disease dataset as an example, initially, a single machine learning prediction model was constructed. Subsequently, six methods such as Pearson, chi-squared, RFE and LightGBM were …comprehensively used for the feature screening. On the basis of the base classifiers, Soft Voting fusion and Stacking fusion was carried out to build a prediction model for cardiovascular diseases, in order to realize an early warning and disease intervention for high-risk populations. To address the data imbalance problem, the SMOTE method was adopted to process the data set, and the prediction effect of the model was analyzed using multi-dimensional and multi-indicators. RESULTS: In the single classifier model, the MLP algorithm performed optimally on the preprocessed heart disease dataset. After feature selection, five features eliminated. The ENSEM_SV algorithm that combines the base classifiers to determine the prediction results by soft voting on the results of the classifiers achieved the optimal value on five metrics such as Accuracy, Jaccard_Score, Hamm_Loss, AUC, etc., and the AUC value reached 0.951. The RF, ET, GBDT, and LGB algorithms were employed in the first stage sub-model composed of base classifiers. The AB algorithm was selected as the second stage model, and the ensemble algorithm ENSEM_ST, obtained by Stacking fusion of the two stages exhibited the best performance on 7 indicators such as Accuracy, Sensitivity, F1_Score, Mathew_Corrcoef, etc., and the AUC reached 0.952. Furthermore, a comparison of the algorithms’ classification effects based on different training set occupancy was carried out. The results indicated that the prediction performance of both the fusion models was better than the single models, and the overall effect of ENSEM_ST fusion was stronger than the ENSEM_SV fusion. CONCLUSIONS: The fusion model established in this study improved the overall classification accuracy and stability of the model to a significant extent. It has a good application value in the predictive analysis of CVD diagnosis, and can provide a valuable reference in the disease diagnosis and intervention strategies. Show more

Keywords: Cardiovascular disease, prediction, supervised learning, comparison, feature selection, ensemble learning

DOI: 10.3233/THC-248021

Citation: Technology and Health Care, vol. 32, no. S1, pp. 241-251, 2024

Authors: Namkoong, Seung | Roh, Hyolyun

Article Type: Review Article

Abstract: BACKGROUND: The dominant feature of Alzheimer’s dementia (AD) is gradual cognitive decline, which can be reflected by reduced finger dexterity. OBJECTIVE: This review analyzed reports on hand function in AD patients to determine the possibility of using it for an early diagnosis and for monitoring the disease progression of AD. METHODS: PubMed, Web of Science, EMBASE, and Cochrane library were searched systematically (search dates: 2000–2022), and relevant articles were cross-checked for related and relevant publications. RESULTS: Seventeen studies assessed the association of the handgrip strength or dexterity with cognitive performance. …The hand dexterity was strongly correlated with the cognitive function in all studies. In the hand dexterity test using the pegboard, there was little difference in the degree of decline in hand function between the healthy elderly (HE) group and the mild cognitive impairment (MCI) group. On the other hand, there was a difference in the hand function between the HE group and the AD group. In addition, the decline in hand dexterity is likely to develop from moderate to severe dementia. In complex hand movements, movement speed variations were greater in the AD than in the HE group, and the automaticity, regularity, and rhythm were reduced. CONCLUSIONS: HE and AD can be identified by a simple hand motion test using a pegboard. The data can be used to predict dementia progression from moderate dementia to severe dementia. An evaluation of complex hand movements can help predict the transition from MCI to AD and the progression from moderate to severe dementia. Show more

Keywords: Alzheimer’s dementia, hand function, hand dexterity, handgrip strength, cognitive impairment

DOI: 10.3233/THC-248022

Citation: Technology and Health Care, vol. 32, no. S1, pp. 253-264, 2024

Authors: Hong, Shichai | Chen, Yihui | Lin, Yue | Xie, Xinsheng | Chen, Gang | Xie, Hefu | Lu, Weifeng

Article Type: Research Article

Abstract: BACKGROUND: This study utilizes machine learning to analyze the recurrence risk of diabetic foot ulcers (DFUs) in elderly diabetic patients, aiming to enhance prevention and intervention efforts. OBJECTIVE: The goal is to construct accurate predictive models for assessing the recurrence risk of DFUs based on high-risk factors, such as age, blood sugar control, alcohol consumption, and smoking, in elderly diabetic patients. METHODS: Data from 138 elderly diabetic patients were collected, and after data cleaning, outlier screening, and feature integration, machine learning models were constructed. Support Vector Machine (SVM) was employed, achieving an accuracy …rate of 93%. RESULTS: Experimental results demonstrate the effectiveness of SVM in predicting the recurrence risk of DFUs in elderly diabetic patients, providing clinicians with a more accurate tool for assessment. CONCLUSIONS: The study highlights the significance of machine learning in managing foot ulcers in elderly diabetic patients, particularly in predicting recurrence risk. This approach facilitates timely intervention, reducing the likelihood of patient recurrence, and introduces computer-assisted medical strategies in elderly diabetes management. Show more

Keywords: Elderly diabetic patients, machine learning, diabetic foot ulcers, recurrence risk

DOI: 10.3233/THC-248023

Citation: Technology and Health Care, vol. 32, no. S1, pp. 265-276, 2024

Authors: Gai, Xinghui | Cai, Huifang | Wang, Junying | Li, Xinyue | Sui, Yan | Liu, Kang | Yang, Dewu

Article Type: Research Article

Abstract: BACKGROUND: Early diagnosis of knee osteoarthritis is an important area of research in the field of clinical medicine. Due to the complexity in the MRI imaging sequences and the diverse structure of cartilage, there are many challenges in the segmentation of knee bone and cartilage. Relevant studies have conducted semantic fusion processing through splicing or summing forms, which results in reduced resolution and the accumulation of redundant information. OBJECTIVE: This study was envisaged to construct an MRI image segmentation model to improve the diagnostic efficiency and accuracy of different grade knee osteoarthritis by adopting the Dual …Attention and Multi-scale Feature Fusion Segmentation network (DA-MFFSnet). METHODS: The feature information of different scales was fused through the Multi-scale Attention Downsample module to extract more accurate feature information, and the Global Attention Upsample module weighted lower-level feature information to reduce the loss of key information. RESULTS: The collected MRI knee images were screened and labeled, and the study results showed that the segmentation effect of DA-MFFSNet model was closer to that of the manually labeled images. The mean intersection over union, the dice similarity coefficient and the volumetric overlap error was 92.74%, 91.08% and 7.44%, respectively, and the accuracy of the differential diagnosis of knee osteoarthritis was 84.42%. CONCLUSIONS: The model exhibited better stability and classification effect. Our results indicated that the Dual Attention and Multi-scale Feature Fusion Segmentation model can improve the segmentation effect of MRI knee images in mild and medium knee osteoarthritis, thereby offering an important clinical value and improving the accuracy of the clinical diagnosis. Show more

Keywords: Coronary atherosclerosis, morphological processing, regional growth, auxiliary diagnosis, vascular stenosis

DOI: 10.3233/THC-248024

Citation: Technology and Health Care, vol. 32, no. S1, pp. 277-286, 2024

Authors: Guo, Kai | Lu, Jingxin | Yang, Hongbo

Article Type: Research Article

Abstract: BACKGROUND: Prosthetic hands have the potential to replace human hands. Using prosthetic hands can help patients with hand loss to complete the necessary daily living actions. OBJECTIVE: This paper studies the design of a bionic, compact, low-cost, and lightweight 3D printing humanoid hand. The five fingers are underactuated, with a total of 9 degrees of freedom. METHODS: In the design of an underactuated hand, it is a basic element composed of an actuator, spring, rope, and guide system. A single actuator is providing power for five fingers. And the dynamic simulation is carried …out to calculate the motion trajectory effect. RESULTS: In this paper, the driving structure of the ultrasonic motor was designed, and the structural size of the ultrasonic motor vibrator was determined by modal and transient simulation analysis, which replace the traditional brake, realize the lightweight design of prosthetic hand, improve the motion accuracy and optimize the driving performance of prosthetic hand. CONCLUSIONS: By replacing traditional actuators with new types of actuators, lightweight design of prosthetic hands can be achieved, improving motion accuracy and optimizing the driving performance of prosthetic hands. Show more

Keywords: Prosthetic hand, ultrasonic motor, analysis

DOI: 10.3233/THC-248025

Citation: Technology and Health Care, vol. 32, no. S1, pp. 287-297, 2024

Authors: Yang, Cuiyun | Bian, Taicheng | Yang, Jin | Hou, Junyi | Cao, Yiliang | Han, Zhihui | Zhao, Xiaoyan | Wen, Weijun | Zhu, Xijun

Article Type: Research Article

Abstract: BACKGROUND: Plane-wave imaging is widely employed in medical imaging due to its ultra-fast imaging speed. However, the image quality is compromised. Existing techniques to enhance image quality tend to sacrifice the imaging frame rate. OBJECTIVE: The study aims to reconstruct high-quality plane-wave images while maintaining the imaging frame rate. METHODS: The proposed method utilizes a U-Net-based generator incorporating a multi-scale convolution module in the encoder to extract information at different levels. Additionally, a Dynamic Criss-Cross Attention (DCCA) mechanism is proposed in the decoder of the U-Net-based generator to extract both local and global …features of plane-wave images while avoiding interference caused by irrelevant regions. RESULTS: In the reconstruction of point targets, the experimental images achieved a reduction in Full Width at Half Maximum (FWHM) of 0.0499 mm, compared to the Coherent Plane-Wave Compounding (CPWC) method using 75-beam plane waves. For the reconstruction of cyst targets, the simulated image achieved a 3.78% improvement in Contrast Ratio (CR) compared to CPWC. CONCLUSIONS: The proposed model effectively addresses the issue of unclear lesion sites in plane-wave images. Show more

Keywords: Reconstruction, multi-scale convolution, dynamic criss-cross attention

DOI: 10.3233/THC-248026

Citation: Technology and Health Care, vol. 32, no. S1, pp. 299-312, 2024

Authors: Lin, Zhong | Yang, Guang-Ming | Ye, Xiu-Bi | Liu, Xiang-Bo | Chen, Song-Sen | Zhang, Yu-Ling | Zhuo, Pi-Qi

Article Type: Research Article

Abstract: BACKGROUND: With the widespread use of low-dose spiral computed tomography (LDCT) and increasing awareness of personal health, the detection rate of pulmonary nodules is steadily rising. OBJECTIVE: To evaluate the success rate and safety of two different models of Hook-Wire needle localization procedures for pulmonary small nodule biopsy. METHODS: Ninety-four cases with a total of 97 pulmonary small nodules undergoing needle localization biopsy were retrospectively analyzed. The cases were divided into two groups: Group A, using breast localization needle steel wire (Bard Healthcare Science Co., Ltd.); Group B, using disposable pulmonary nodule puncture …needle (SensCure Biotechnology Co., Ltd.). All patients underwent video-assisted thoracoscopic surgery (VATS) for nodule removal on the same day after localization and biopsy. The puncture localization operation time, success rate, complications such as pulmonary hemorrhage, pneumothorax, hemoptysis, and postoperative comfort were observed and compared. RESULTS: In Group A, the average localization operation time for 97 nodules was 15.47 ± 5.31 minutes, with a success rate of 94.34%. The complication rate was 71.69% (12 cases of pneumothorax, 35 cases of pulmonary hemorrhage, 2 cases of hemoptysis), and 40 cases of post-localization discomfort were reported. In Group B, the average localization operation time was 25.32 ± 7.83 minutes, with a 100% success rate. The complication rate was 29.55% (3 cases of pneumothorax, 15 cases of pulmonary hemorrhage, 0 cases of hemoptysis), and 3 cases reported postoperative discomfort. According to the data analysis in this study, Group B had a lower incidence of puncture-related complications than Group A, along with a higher success rate and significantly greater postoperative comfort. CONCLUSIONS: The disposable pulmonary nodule puncture needle is safer and more effective in pulmonary small nodule localization biopsy, exhibiting increased comfort compared to the breast localization needle. Additionally, the incidence of complications is significantly lower. Show more

Keywords: CT-guided, pulmonary small nodule, needle localization, Hook-Wire needle

DOI: 10.3233/THC-248027

Citation: Technology and Health Care, vol. 32, no. S1, pp. 313-320, 2024

Authors: Yin, Shuang | Han, Fujun | Zhou, Chenliang | Zhao, Yonghou

Article Type: Research Article

Abstract: BACKGROUND: The process of wound healing is complex, and expediting it remains a challenge. The advantages of extremely low frequency electric and magnetic fields (ELF-EMF) are its non-invasive treatment, promotes healing and promotes myogenesis of C2C12 cells. Epidermal growth factor (EGF) is known to play a vital role in promoting wound healing, so a combination of ELF-EMF and EGF can have far-reaching significance. OBJECTIVE: To study the effect of recombinant murine epidermal growth factor (rmEGF) combined with ELF-EMF on wound healing. METHODS: Thirty-six rats were randomly divided into three groups: normal control group, EGF …group, and ELF-EMF+ EGF group, and a 20 mm × 20 mm dorsal wound was made. The wound healing rate of rats was calculated on the 3rd, 7th, 11th and 15th day. HE staining was used to observe the micro-morphological changes during the wound healing process. RESULTS: The wound healing rate of EGF+ ELF-EMF group was better than other groups. On the 15th day of wound healing, the wounds of each group were completely healed. On the 3rd, 7th, 11th and 15th day of HE staining, the early inflammatory cell infiltration, the arrangement of fibroblasts and the number of new capillaries in the wounds of EGF+ ELF-EMF group were better than those of the other groups. CONCLUSIONS: rmEGF combined with ELF-EMF significantly promotes wound healing in SD rats. Show more

Keywords: Extremely low frequency electromagnetic field, ELF-EMF, recombinant murine epidermal growth factor, rmEGF, epidermal growth factor, EGF, epidermal growth factor receptor, EGFR, sprague-dawley rats, SD rats

DOI: 10.3233/THC-248028

Citation: Technology and Health Care, vol. 32, no. S1, pp. 321-328, 2024

Authors: Zhang, Qiaohui | Xu, Demin

Article Type: Research Article

Abstract: BACKGROUND: Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12 patient features. By finely modeling continuous medical records, we successfully enhanced the accuracy of mortality prediction. OBJECTIVE: The objective of this research was to leverage the Seq2Seq model in conjunction with patient features for precise mortality prediction in heart failure cases, surpassing the performance of traditional machine learning methods. METHODS: The study utilized a Seq2Seq model …in deep learning, incorporating 12 patient features, to intricately model continuous medical records. The experimental design aimed to compare the performance of Seq2Seq with traditional machine learning methods in predicting mortality rates. RESULTS: The experimental results demonstrated that the Seq2Seq model outperformed conventional machine learning methods in terms of predictive accuracy. Feature importance analysis provided critical patient risk factors, offering robust support for formulating personalized treatment plans. CONCLUSIONS: This research sheds light on the significant applications of deep learning, specifically the Seq2Seq model, in enhancing the precision of mortality prediction in heart failure cases. The findings present a valuable direction for the application of deep learning in the medical field and provide crucial insights for future research and clinical practices. Show more

Keywords: Heart failure, Seq2Seq model, medical decision support, mortality prediction

DOI: 10.3233/THC-248029

Citation: Technology and Health Care, vol. 32, no. S1, pp. 329-337, 2024