BioMedical Engineering OnLine最新文献

{"title":"A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images","authors":"Laifa Yan, Shan Ling, Rongsong Mao, Haoran Xi, Fei Wang","doi":"10.1186/s12938-024-01230-2","DOIUrl":"https://doi.org/10.1186/s12938-024-01230-2","url":null,"abstract":"Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis. In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels—the pulmonary artery, aorta, and superior vena cava—in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images. We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively. Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"32 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140576904","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":"Noninvasive spinal stimulation improves walking in chronic stroke survivors: a proof-of-concept case series.","authors":"Yaejin Moon, Chen Yang, Nicole C Veit, Kelly A McKenzie, Jay Kim, Shreya Aalla, Lindsey Yingling, Kristine Buchler, Jasmine Hunt, Sophia Jenz, Sung Yul Shin, Ameen Kishta, V Reggie Edgerton, Yury P Gerasimenko, Elliot J Roth, Richard L Lieber, Arun Jayaraman","doi":"10.1186/s12938-024-01231-1","DOIUrl":"10.1186/s12938-024-01231-1","url":null,"abstract":"<p><strong>Background: </strong>After stroke, restoring safe, independent, and efficient walking is a top rehabilitation priority. However, in nearly 70% of stroke survivors asymmetrical walking patterns and reduced walking speed persist. This case series study aims to investigate the effectiveness of transcutaneous spinal cord stimulation (tSCS) in enhancing walking ability of persons with chronic stroke.</p><p><strong>Methods: </strong>Eight participants with hemiparesis after a single, chronic stroke were enrolled. Each participant was assigned to either the Stim group (N = 4, gait training + tSCS) or Control group (N = 4, gait training alone). Each participant in the Stim group was matched to a participant in the Control group based on age, time since stroke, and self-selected gait speed. For the Stim group, tSCS was delivered during gait training via electrodes placed on the skin between the spinous processes of C5-C6, T11-T12, and L1-L2. Both groups received 24 sessions of gait training over 8 weeks with a physical therapist providing verbal cueing for improved gait symmetry. Gait speed (measured from 10 m walk test), endurance (measured from 6 min walk test), spatiotemporal gait symmetries (step length and swing time), as well as the neurophysiological outcomes (muscle synergy, resting motor thresholds via spinal motor evoked responses) were collected without tSCS at baseline, completion, and 3 month follow-up.</p><p><strong>Results: </strong>All four Stim participants sustained spatiotemporal symmetry improvements at the 3 month follow-up (step length: 17.7%, swing time: 10.1%) compared to the Control group (step length: 1.1%, swing time 3.6%). Additionally, 3 of 4 Stim participants showed increased number of muscle synergies and/or lowered resting motor thresholds compared to the Control group.</p><p><strong>Conclusions: </strong>This study provides promising preliminary evidence that using tSCS as a therapeutic catalyst to gait training may increase the efficacy of gait rehabilitation in individuals with chronic stroke. Trial registration NCT03714282 (clinicaltrials.gov), registration date: 2018-10-18.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"38"},"PeriodicalIF":3.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10986021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140334617","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":"Classification of vasovagal syncope from physiological signals on tilt table testing.","authors":"Mahbuba Ferdowsi, Ban-Hoe Kwan, Maw Pin Tan, Nor' Izzati Saedon, Sukanya Subramaniam, Noor Fatin Izzati Abu Hashim, Siti Sakinah Mohd Nasir, Imran Zainal Abidin, Kok Han Chee, Choon-Hian Goh","doi":"10.1186/s12938-024-01229-9","DOIUrl":"10.1186/s12938-024-01229-9","url":null,"abstract":"<p><strong>Background: </strong>The diagnostic test for vasovagal syncope (VVS), the most common cause of syncope is head-up tilt test (HUTT) assessment. During the test, subjects experienced clinical symptoms such as nausea, sweating, pallor, the feeling of palpitations, being on the verge of passing out, and fainting. The study's goal is to develop an algorithm to classify VVS patients based on physiological signals blood pressure (BP) and electrocardiography (ECG) obtained from the HUTT.</p><p><strong>Methods: </strong>After 10 min of supine rest, the subject was tilted at a 70-degree angle on a tilt table for approximately a total of 35 min. 400 µg of glyceryl trinitrate (GTN) was administered sublingually after the first 20 min and monitoring continued for another 15 min. Mean imputation and K-nearest neighbors (KNN) imputation approaches to handle missing values. Next, feature selection techniques were implemented, including genetic algorithm, recursive feature elimination, and feature importance, to determine the crucial features. The Mann-Whitney U test was then performed to determine the statistical difference between two groups. Patients with VVS are categorized via machine learning models including Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), KNN, Logistic Regression (LR), and Random Forest (RF). The developed model is interpreted using an explainable artificial intelligence (XAI) model known as partial dependence plot.</p><p><strong>Results: </strong>A total of 137 subjects aged between 9 and 93 years were recruited for this study, 54 experienced clinical symptoms were considered positive tests, while the remaining 83 tested negative. Optimal results were obtained by combining the KNN imputation technique and three tilting features with SVM with 90.5% accuracy, 87.0% sensitivity, 92.7% specificity, 88.6% precision, 87.8% F1 score, and 95.4% ROC (receiver operating characteristics) AUC (area under curve).</p><p><strong>Conclusions: </strong>The proposed algorithm effectively classifies VVS patients with over 90% accuracy. However, the study was confined to a small sample size. More clinical datasets are required to ensure that our approach is generalizable.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"37"},"PeriodicalIF":3.9,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10981362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140329594","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":"Effects of immediate loading directionality on the mechanical sensing protein PIEZO1 expression and early-stage healing process of peri-implant bone","authors":"Chuanyuan Mao, Weijun Yu, Guanglong Li, Ziyuan Xu, Yuhua Gong, Min Jin, Eryi Lu","doi":"10.1186/s12938-024-01223-1","DOIUrl":"https://doi.org/10.1186/s12938-024-01223-1","url":null,"abstract":"The reduced treatment time of dental implants with immediate loading protocol is an appealing solution for dentists and patients. However, there remains a significant risk of early peri-implant bone response following the placement of immediately loaded implants, and limited information is available regarding loading directions and the associated in vivo characteristics of peri-implant bone during the early stages. This study aimed to investigate the effects of immediate loading directionality on the expression of mechanical sensing protein PIEZO1 and the healing process of peri-implant bone in the early stage. Thirty-two implants were inserted into the goat iliac crest models with 10 N static lateral immediate loading applied, followed by histological, histomorphological, immunohistochemical, X-ray microscopy and energy dispersive X-ray spectroscopy evaluations conducted after 10 days. From evaluations at the cellular, tissue, and organ levels, it was observed that the expression of mechanical sensing protein PIEZO1 in peri-implant bone was significantly higher in the compressive side compared to the tensile side. This finding coincided with trends observed in interfacial bone extracellular matrix (ECM) contact percentage, bone mass, and new bone formation. This study provides a novel insight into the immediate loading directionality as a potential influence factor for dental implant treatments by demonstrating differential effects on the mechanical sensing protein PIEZO1 expression and related early-stage healing processes of peri-implant bone. Immediate loading directions serve as potential therapeutic influence factors for peri-implant bone during its early healing stage.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"34 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165471","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":"Enhancing automated lower limb rehabilitation exercise task recognition through multi-sensor data fusion in tele-rehabilitation","authors":"Alireza Ettefagh, Atena Roshan Fekr","doi":"10.1186/s12938-024-01228-w","DOIUrl":"https://doi.org/10.1186/s12938-024-01228-w","url":null,"abstract":"Tele-rehabilitation is the provision of physiotherapy services to individuals in their own homes. Activity recognition plays a crucial role in the realm of automatic tele-rehabilitation. By assessing patient movements, identifying exercises, and providing feedback, these platforms can offer insightful information to clinicians, thereby facilitating an improved plan of care. This study introduces a novel deep learning approach aimed at identifying lower limb rehabilitation exercises. This is achieved through the integration of depth data and pressure heatmaps. We hypothesized that combining pressure heatmaps and depth data could improve the model’s overall performance. In this study, depth videos and body pressure data from an accessible online dataset were used. This dataset comprises data from 30 healthy individuals performing 7 lower limb rehabilitation exercises. To accomplish the classification task, three deep learning models were developed, all based on an established 3D-CNN architecture. The models were designed to classify the depth videos, sequences of pressure data frames, and combination of depth videos and pressure frames. The models’ performance was assessed through leave-one-subject-out and leave-multiple-subjects-out cross-validation methods. Performance metrics, including accuracy, precision, recall, and F1 score, were reported for each model. Our findings indicated that the model trained on the fusion of depth and pressure data showed the highest and most stable performance when compared with models using individual modality inputs. This model could effectively identify the exercises with an accuracy of 95.71%, precision of 95.83%, recall of 95.71%, and an F1 score of 95.74%. Our results highlight the impact of data fusion for accurately classifying lower limb rehabilitation exercises. We showed that our model could capture different aspects of exercise movements using the visual and weight distribution data from the depth camera and pressure mat, respectively. This integration of data provides a better representation of exercise patterns, leading to higher classification performance. Notably, our results indicate the potential application of this model in automatic tele-rehabilitation platforms.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"33 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165538","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":"Construction of in vitro liver-on-a-chip models and application progress.","authors":"Jie Liu, Yimei Du, Xinxin Xiao, Daopeng Tan, Yuqi He, Lin Qin","doi":"10.1186/s12938-024-01226-y","DOIUrl":"10.1186/s12938-024-01226-y","url":null,"abstract":"<p><p>The liver is the largest internal organ of the human body. It has a complex structure and function and plays a vital role in drug metabolism. In recent decades, extensive research has aimed to develop in vitro models that can simulate liver function to demonstrate changes in the physiological and pathological environment of the liver. Animal models and in vitro cell models are common, but the data obtained from animal models lack relevance when applied to humans, while cell models have limited predictive ability for metabolism and toxicity in humans. Recent advancements in tissue engineering, biomaterials, chip technology, and 3D bioprinting have provided opportunities for further research in in vitro models. Among them, liver-on-a-Chip (LOC) technology has made significant achievements in reproducing the in vivo behavior, physiological microenvironment, and metabolism of cells and organs. In this review, we discuss the development of LOC and its research progress in liver diseases, hepatotoxicity tests, and drug screening, as well as chip combinations. First, we review the structure and the physiological function of the liver. Then, we introduce the LOC technology, including general concepts, preparation materials, and methods. Finally, we review the application of LOC in disease modeling, hepatotoxicity tests, drug screening, and chip combinations, as well as the future challenges and directions of LOC.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"33"},"PeriodicalIF":3.9,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10941602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140139815","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":"A novel in-bed body posture monitoring for decubitus ulcer prevention using body pressure distribution mapping.","authors":"Lindsay Stern, Geoff Fernie, Atena Roshan Fekr","doi":"10.1186/s12938-024-01227-x","DOIUrl":"10.1186/s12938-024-01227-x","url":null,"abstract":"<p><strong>Background: </strong>Decubitus ulcers are prevalent among the aging population due to a gradual decline in their overall health, such as nutrition, mental health, and mobility, resulting in injury to the skin and tissue. The most common technique to prevent these ulcers is through frequent repositioning to redistribute body pressures. Therefore, the main goal of this study is to facilitate the timely repositioning of patients through the use of a pressure mat to identify in-bed postures in various sleep environments. Pressure data were collected from 10 healthy participants lying down on a pressure mat in 19 various in-bed postures, correlating to the supine, prone, right-side, and left-side classes. In addition, pressure data were collected from participants sitting at the edge of the bed as well as an empty bed. Each participant was asked to lie in these 19 postures in three distinct testing environments: a hospital bed, a home bed, and a home bed with a foam mattress topper. To categorize each posture into its respective class, the pre-trained 2D ResNet-18 CNN and the pre-trained Inflated 3D CNN algorithms were trained and validated using image and video pressure mapped data, respectively.</p><p><strong>Results: </strong>The ResNet-18 and Inflated 3D CNN algorithms were validated using leave-one-subject-out (LOSO) and leave-one-environment-out (LOEO) cross-validation techniques. LOSO provided an average accuracy of 92.07% ± 5.72% and 82.22% ± 8.50%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively. Contrastingly, LOEO provided a reduced average accuracy of 85.37% ± 14.38% and 77.79% ± 9.76%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively.</p><p><strong>Conclusion: </strong>These pilot results indicate that the proposed algorithms can accurately distinguish between in-bed postures, on unseen participant data as well as unseen mattress environment data. The proposed algorithms can establish the basis of a decubitus ulcer prevention platform that can be applied to various sleeping environments. To the best of our knowledge, the impact of mattress stiffness has not been considered in previous studies regarding in-bed posture monitoring.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"34"},"PeriodicalIF":3.9,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10941443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140139814","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":"Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting.","authors":"Juzhao Zhang, Xuan Luo, Deshang Li, Yajun Peng, Guiling Gao, Liangwen Lei, Meng Gao, Lina Lu, Yi Xu, Tao Yu, Senlin Lin, Yingyan Ma, Chunxia Yao, Haidong Zou","doi":"10.1186/s12938-024-01222-2","DOIUrl":"10.1186/s12938-024-01222-2","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators.</p><p><strong>Design: </strong>Prospective cross-sectional study.</p><p><strong>Methods: </strong>In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography.</p><p><strong>Results: </strong>There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above.</p><p><strong>Conclusions: </strong>The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"32"},"PeriodicalIF":3.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10935825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109070","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":"Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention","authors":"Junyu Mi, Rui Wang, Qian Feng, Lin Han, Yan Zhuang, Ke Chen, Zhong Chen, Zhan Hua, Yan luo, Jiangli Lin","doi":"10.1186/s12938-024-01215-1","DOIUrl":"https://doi.org/10.1186/s12938-024-01215-1","url":null,"abstract":"Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"34 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140098924","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":"Effects of hyperbaric oxygen combined cabin ventilator on critically ill patients with liberation difficulty after tracheostomy","authors":"Yinliang Qi, Jixiang Xu, Hui Liu, Xiaomei Zhou","doi":"10.1186/s12938-024-01220-4","DOIUrl":"https://doi.org/10.1186/s12938-024-01220-4","url":null,"abstract":"Critically ill patients undergoing liberation often encounter various physiological and clinical complexities and challenges. However, whether the combination of hyperbaric oxygen and in-cabin ventilator therapy could offer a comprehensive approach that may simultaneously address respiratory and potentially improve outcomes in this challenging patient population remain unclear. This retrospective study involved 148 patients experiencing difficulty in liberation after tracheotomy. Inclusion criteria comprised ongoing mechanical ventilation need, lung inflammation on computed tomography (CT) scans, and Glasgow Coma Scale (GCS) scores of ≤ 9. Exclusion criteria excluded patients with active bleeding, untreated pneumothorax, cerebrospinal fluid leakage, and a heart rate below 50 beats per minute. Following exclusions, 111 cases were treated with hyperbaric oxygen combined cabin ventilator, of which 72 cases were successfully liberated (SL group) and 28 cases (NSL group) were not successfully liberated. The hyperbaric oxygen chamber group received pressurization to 0.20 MPa (2.0 ATA) for 20 min, followed by 60 min of ventilator oxygen inhalation. Successful liberation was determined by a strict process, including subjective and objective criteria, with a prolonged spontaneous breathing trial. GCS assessments were conducted to evaluate consciousness levels, with scores categorized as normal, mildly impaired, moderately impaired, or severely impaired. Patients who underwent treatment exhibited improved GCS, blood gas indicators, and cardiac function indexes. The improvement of GCS, partial pressure of oxygen (PaO2), oxygen saturation of blood (SaO2), oxygenation index (OI) in the SL group was significantly higher than that of the NSL group. However, there was no significant difference in the improvement of left ventricular ejection fraction (LVEF), left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), and stroke volume (SV) between the SL group and the NSL group after treatment. Hyperbaric oxygen combined with in-cabin ventilator therapy effectively enhances respiratory function, cardiopulmonary function, and various indicators of critically ill patients with liberation difficulty after tracheostomy.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"278 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140056558","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}