Technology and Health Care最新文献

{"title":"UPLC-Q-Exactive Orbitrap-MS and network pharmacology for deciphering the active compounds and mechanisms of stir-fried Raphani Semen in treating functional dyspepsia.","authors":"Zhuang Miao, Xinyue Yu, Lizhen Zhang, Liqiao Zhu, Huagang Sheng","doi":"10.3233/THC-231122","DOIUrl":"10.3233/THC-231122","url":null,"abstract":"<p><strong>Background: </strong>As a traditional digestive medicine, stir-fried Raphani Semen (SRS) has been used to treat food retention for thousands of years in China. Modern research has shown that SRS has a good therapeutic effect on functional dyspepsia (FD). However, the active components and mechanism of SRS in the treatment of FD are still unclear.</p><p><strong>Objective: </strong>The purpose of this study is to elucidate the material basis and mechanism of SRS for treating FD based on UPLC-Q-Exactive Orbitrap MS/MS combined with network pharmacology and molecular docking.</p><p><strong>Methods: </strong>The compounds of SRS water decoction were identified by UPLC-Q-Exactive Orbitrap MS/MS and the potential targets of these compounds were predicted by Swiss Target Prediction. FD-associated targets were collected from disease databases. The overlapped targets of SRS and FD were imported into STRING to construct Protein-Protein Interaction (PPI) network. Then, the Metascape was used to analyze Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway after introducing overlapped targets. Finally, the active components and core targets were obtained by analyzing the \"component-target-pathway\" network, and the affinity between them was verified by molecular docking.</p><p><strong>Results: </strong>53 components were identified, and 405 targets and 1487 FD-related targets were collected. GO and KEGG analysis of 174 overlapped targets showed that SRS had important effects on hormone levels, serotonin synapses, calcium signaling pathway and cAMP signaling pathway. 7 active components and 15 core targets were screened after analyzing the composite network. Molecular docking results showed that multiple active components had high affinity with most core targets.</p><p><strong>Conclusion: </strong>SRS can treat FD through a variety of pathways, which provides a direction for the modern application of SRS in FD treatment.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140190298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19.","authors":"Almir Badnjević, Lejla Gurbeta Pokvić, Merima Smajlhodžić-Deljo, Lemana Spahić, Tamer Bego, Neven Meseldžić, Lejla Prnjavorac, Besim Prnjavorac, Omer Bedak","doi":"10.3233/THC-230917","DOIUrl":"10.3233/THC-230917","url":null,"abstract":"<p><strong>Background: </strong>With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients.</p><p><strong>Objective: </strong>The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage.</p><p><strong>Methods: </strong>An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina.</p><p><strong>Results: </strong>The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections.</p><p><strong>Conclusion: </strong>The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41240148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a clinical prediction model for glioma grade using machine learning.","authors":"Mingzhen Wu, Jixin Luan, Di Zhang, Hua Fan, Lishan Qiao, Chuanchen Zhang","doi":"10.3233/THC-231645","DOIUrl":"10.3233/THC-231645","url":null,"abstract":"<p><strong>Background: </strong>Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.</p><p><strong>Objective: </strong>This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading.</p><p><strong>Methods: </strong>Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model.</p><p><strong>Results: </strong>The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit.</p><p><strong>Conclusion: </strong>A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139673390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical application of two types of Hook-Wire needle localization procedures for pulmonary small nodule biopsy.","authors":"Zhong Lin, Guang-Ming Yang, Xiu-Bi Ye, Xiang-Bo Liu, Song-Sen Chen, Yu-Ling Zhang, Pi-Qi Zhuo","doi":"10.3233/THC-248027","DOIUrl":"10.3233/THC-248027","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>To evaluate the success rate and safety of two different models of Hook-Wire needle localization procedures for pulmonary small nodule biopsy.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140855746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection method for unrecognized spatial disorientation based on optical flow stimuli.","authors":"Chenru Hao, Rui Su, Chunnan Dong, Jingjing Zhang, Ziqiang Chi, Fanzhen Meng, Ruibin Zhao, Yanru Wu, Linlin Wang, Pengfei Li, Chengwei Chen, Qingjie Lian, Li Cheng","doi":"10.3233/THC-248030","DOIUrl":"10.3233/THC-248030","url":null,"abstract":"<p><strong>Background: </strong>Flight accidents caused by spatial disorientation (SD) greatly affect flight safety.</p><p><strong>Objective: </strong>Few studies have been devoted to the evaluation of SD.</p><p><strong>Methods: </strong>10 pilots and 10 non-pilots were recruited for the experimental induction of SD. Videos for giving optical flow stimuli were played at two different flow speeds to induce SD. Subjective judgment and center of foot pressure (CoP) data were collected from the tests. The data were combined to determine the occurrence of SD and analyze the SD types.</p><p><strong>Results: </strong>The number of self-reported SD events was slightly smaller in the pilots than in the non-pilots. The average upper bound of the confidence interval for the standard deviation of CoP was 0.32 ± 0.09 cm and 0.38 ± 0.12 cm in the pilots and non-pilots, respectively. This indicator was significantly lower in the pilots than in the non-pilots (P= 0.03). The success rate of the experimental induction of unrecognized SD was 26.7% and 45.0% in the pilots and non-pilots, respectively.</p><p><strong>Conclusion: </strong>The method offered a new to analyze unrecognized SD. We could determine the occurrence unrecognized SD. This is an essential means of reducing flight accidents caused by unrecognized SD.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of deep learning models in precision prediction of survival rates for heart failure patients.","authors":"Qiaohui Zhang, Demin Xu","doi":"10.3233/THC-248029","DOIUrl":"10.3233/THC-248029","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140959789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between physical activity and functional movement screening among university students in an adaptive physical course.","authors":"Fan Yang, Pengzhi Sang, Xiaojing Shen, Sanjun Yang, Yunchen Meng, Huiming Hu","doi":"10.3233/THC-248012","DOIUrl":"10.3233/THC-248012","url":null,"abstract":"<p><strong>Background: </strong>Physical activity (PA) holds profound implications for the holistic development of college students. However, students with chronic diseases or physical disabilities experience significantly limited PA during adaptive sports.</p><p><strong>Objective: </strong>This study aims to investigate the relationship between physical activity and Functional Movement Screening (FMS) among university students who participate in the adaptive physical course.</p><p><strong>Methods: </strong>36 university students (from the adaptive physical course) completed the International Physical Activity Questionnaire-Long Form (IPAQ-L). Body measurements and FMS were assessed. Correlation analysis and t-tests were used to determine relationships and differences between various indicators. A two-way analysis of variance was used to investigate potential variations in FMS scores based on gender and weight status.</p><p><strong>Results: </strong>The results show that gender, PA, and BMI significantly influence FMS scores in students participating in adaptive physical courses. FMS score is significantly negatively correlated with BMI and significantly positively correlated with PA. The FMS score for males, as well as the scores for Trunk Stability Push-Up and Rotary Stability, are significantly higher than those for females.</p><p><strong>Conclusion: </strong>University students in adaptive physical courses can benefit from increased PA and FMS scores. Improving functional movement and enhancing physical activity are crucial for promoting overall health in this population.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140959793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of supervised learning algorithms for prediction of cardiovascular diseases.","authors":"Yifeng Dou, Jiantao Liu, Wentao Meng, Yingchao Zhang","doi":"10.3233/THC-248021","DOIUrl":"10.3233/THC-248021","url":null,"abstract":"<p><strong>Background: </strong>With the advent of artificial intelligence technology, machine learning algorithms have been widely used in the area of disease prediction.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140959805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Function of the hand as a predictor of early diagnosis and progression of Alzheimer's dementia: A systematic review.","authors":"Seung Namkoong, Hyolyun Roh","doi":"10.3233/THC-248022","DOIUrl":"10.3233/THC-248022","url":null,"abstract":"<p><strong>Background: </strong>The dominant feature of Alzheimer's dementia (AD) is gradual cognitive decline, which can be reflected by reduced finger dexterity.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of intra-operative fluorescence imaging in periprosthetic joint infection: State of the art and future perspectives.","authors":"Domenico De Mauro, Jochen Salber, Davide Stimolo, Ende Florian, Mustafa Citak","doi":"10.3233/THC-240479","DOIUrl":"10.3233/THC-240479","url":null,"abstract":"<p><strong>Background: </strong>In periprosthetic joint infections (PJIs), the surgeon's role becomes pivotal in addressing the infection locally, necessitating the surgical removal of infected and necrotic tissue. Opportunity to enhance the visualization of infected tissue during surgery could represent a game-changing innovation.</p><p><strong>Objective: </strong>The aim of this narrative review is to delineate the application of intraoperative fluorescence imaging for targeting infected tissues in PJIs.</p><p><strong>Methods: </strong>A systematic review, adhering to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, was carried out. The search included multiple online database; MEDLINE, Scopus, and Web of Science. For data extraction the following were evaluated: (i) diagnosis of musculoskeletal infection; (ii) use of intraoperative fluorescence imaging; (iii) infected or necrotic tissues as target.</p><p><strong>Results: </strong>Initially, 116 studies were identified through online database searches and reference investigations. The search was narrowed down to a final list of 5 papers for in-depth analysis at the full-text level. Subsequently, 2 studies were included in the review. The study included a total of 13 patients, focusing on cases of fracture-related infections of the lower limbs.</p><p><strong>Conclusion: </strong>The primary and crucial role for orthopedic surgeons in PJIs is the surgical debridement and precise removal of necrotic and infected tissue. Technologies that enable clear and accurate visualization of the tissue to be removed can enhance the eradication of infections, thereby promoting healing. A promising avenue for the future involves the potential application of intraoperative fluorescence imaging in pursuit of this objective.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}