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18 pages, 401 KiB  
Article
Physiotherapy in Prehabilitation for Bariatric Surgery—Analysis of Its Impact on Functional Capacity and Original Predictive Models of Functional Status Outcome
by Katarzyna Gierat-Haponiuk, Piotr Wąż, Julia Haponiuk-Skwarlińska, Maciej Wilczyński and Ireneusz Haponiuk
J. Clin. Med. 2025, 14(15), 5265; https://doi.org/10.3390/jcm14155265 - 25 Jul 2025
Viewed by 248
Abstract
Background/Objectives: Prehabilitation is a multimodal intervention introduced in preparation for various surgical procedures. The most effective treatment for obesity is bariatric surgery. Physiotherapy during prehabilitation for bariatric surgery may be an effective method of functional capacity improvement. We aimed to evaluate the [...] Read more.
Background/Objectives: Prehabilitation is a multimodal intervention introduced in preparation for various surgical procedures. The most effective treatment for obesity is bariatric surgery. Physiotherapy during prehabilitation for bariatric surgery may be an effective method of functional capacity improvement. We aimed to evaluate the impact of an individual outpatient 12-week, exercise-based physiotherapy program featuring prehabilitation on functional status, exercise tolerance, everyday mobility, and fatigue among patients qualified for bariatric surgery. Methods: The completion of an individual outpatient 12-week, exercise-based physiotherapy program during prehabilitation was an inclusion criterion for the study group. Participants included in the study and control groups were assessed twice, after enrollment into the prehabilitation program (the first assessment) and after prehabilitation but before surgery (the second assessment). Both assessments involved functional tests (a six-minute walking test [6MWT], a timed up and go test [TUG], a chest mobility test, anthropometric measures, a mobility index [Barthel], and a modified Borg scale). The collected anthropometric data and values from the 6MWT were used to create original linear models. This study followed STROBE recommendations. Results: The study group and control group did not differ statistically in terms of their anthropometric data. Statistically significant results were obtained between the first and second assessments in both groups in terms of body weight and waist circumference. However, only the study group showed improved results in the TUG test (p = 0.0001) and distance in the 6MWT (p = 0.0005). The study group presented with the normalization of blood pressure (BP) after exertion in the second assessment (systolic BP p = 0.0204; diastolic BP p = 0.0377), and the 6MWT results were close to the norms. According to the original linear model used to predict performance in the 6MWT, the primary modifiable determinant of exercise tolerance was the participant’s weight, while gender served as a non-modifiable determinant. Conclusions: Exercise-based physiotherapy in prehabilitation was associated with improved functional capacity in patients preparing for bariatric surgery, contributing to the improvement in 6MWT results in relation to the norms as well as exercise tolerance. Body weight may be an independent factor determining distance in the 6MWT for patients undergoing prehabilitation for bariatric surgery. Full article
(This article belongs to the Special Issue Clinical Advances in Obesity and Bariatric Surgery)
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21 pages, 899 KiB  
Article
Cervical Spine Range of Motion Reliability with Two Methods and Associations with Demographics, Forward Head Posture, and Respiratory Mechanics in Patients with Non-Specific Chronic Neck Pain
by Petros I. Tatsios, Eirini Grammatopoulou, Zacharias Dimitriadis, Irini Patsaki, George Gioftsos and George A. Koumantakis
J. Funct. Morphol. Kinesiol. 2025, 10(3), 269; https://doi.org/10.3390/jfmk10030269 - 16 Jul 2025
Cited by 1 | Viewed by 359
Abstract
Objectives: New smartphone-based methods for measuring cervical spine range of motion (CS-ROM) and posture are emerging. The purpose of this study was to assess the reliability and validity of three such methods in patients with non-specific chronic neck pain (NSCNP). Methods: [...] Read more.
Objectives: New smartphone-based methods for measuring cervical spine range of motion (CS-ROM) and posture are emerging. The purpose of this study was to assess the reliability and validity of three such methods in patients with non-specific chronic neck pain (NSCNP). Methods: The within-day test–retest reliability of CS-ROM and forward head posture (craniovertebral angle-CVA) was examined in 45 patients with NSCNP. CS-ROM was simultaneously measured with an accelerometer sensor (KFORCE Sens®) and a mobile phone device (iHandy and Compass apps), testing the accuracy of each and the parallel-forms reliability between the two methods. For construct validity, correlations of CS-ROM with demographics, lifestyle, and other cervical and thoracic spine biomechanically based measures were examined in 90 patients with NSCNP. Male–female differences were also explored. Results: Both methods were reliable, with measurements concurring between the two devices in all six movement directions (intraclass correlation coefficient/ICC = 0.90–0.99, standard error of the measurement/SEM = 0.54–3.09°). Male–female differences were only noted for two CS-ROM measures and CVA. Significant associations were documented: (a) between the six CS-ROM measures (R = 0.22–0.54, p < 0.05), (b) participants’ age with five out of six CS-ROM measures (R = 0.23–0.40, p < 0.05) and CVA (R = 0.21, p < 0.05), (c) CVA with two out of six CS-ROM measures (extension R = 0.29, p = 0.005 and left-side flexion R = 0.21, p < 0.05), body mass (R = −0.39, p < 0.001), body mass index (R = −0.52, p < 0.001), and chest wall expansion (R = 0.24–0.29, p < 0.05). Significantly lower forward head posture was noted in subjects with a high level of physical activity relative to those with a low level of physical activity. Conclusions: The reliability of both CS-ROM methods was excellent. Reductions in CS-ROM and increases in CVA were age-dependent in NSCNP. The significant relationship identified between CVA and CWE possibly signifies interconnections between NSCNP and the biomechanical aspect of dysfunctional breathing. Full article
(This article belongs to the Section Kinesiology and Biomechanics)
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17 pages, 2244 KiB  
Article
Associations Between Daily Heart Rate Variability and Self-Reported Wellness: A 14-Day Observational Study in Healthy Adults
by James Hannon, Adrian O’Hagan, Rory Lambe, Ben O’Grady and Cailbhe Doherty
Sensors 2025, 25(14), 4415; https://doi.org/10.3390/s25144415 - 15 Jul 2025
Viewed by 825
Abstract
Heart rate variability (HRV), particularly the root mean square of successive differences (RMSSD), is widely used as a non-invasive indicator of autonomic nervous system activity and physiological recovery. This study examined whether daily short-term HRV, measured under standardised morning conditions, was associated with [...] Read more.
Heart rate variability (HRV), particularly the root mean square of successive differences (RMSSD), is widely used as a non-invasive indicator of autonomic nervous system activity and physiological recovery. This study examined whether daily short-term HRV, measured under standardised morning conditions, was associated with self-reported wellness in a non-clinical adult population. Over a 14-day period, 41 participants completed daily five-minute HRV recordings using a Polar H10 chest sensor and the Kubios mobile app, followed by ratings of sleep quality, fatigue, stress, and physical recovery. Bayesian ordinal mixed-effects models revealed that higher RMSSD values were associated with better self-reported sleep (β = 0.510, 95% HDI: 0.239 to 0.779), lower fatigue (β = 0.281, 95% HDI: 0.020 to 0.562), and reduced stress (β = 0.353, 95% HDI: 0.059 to 0.606), even after adjusting for covariates. No association was found between RMSSD and perceived muscle soreness. These findings support the interpretability of RMSSD as a physiological marker of daily recovery and stress in real-world settings. While the effect sizes were modest and individual variability remained substantial, results suggest that consistent HRV monitoring may offer meaningful insight into subjective wellness—particularly when contextualised and tracked over time. Full article
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16 pages, 7728 KiB  
Article
A Chest Strap-Based System for Electrocardiogram Monitoring
by Xiaoman Zhang, Yaoliang Zhan, Xue Wang and Jin Yang
Appl. Sci. 2025, 15(11), 5920; https://doi.org/10.3390/app15115920 - 24 May 2025
Viewed by 489
Abstract
To address the issues of poor comfort and limited mobility associated with traditional ECG monitoring systems, this study developed a chest strap ECG monitoring system (CEMS) utilizing silver-coated polyamide yarn. This system can continuously capture high-quality ECG signals during daily activities such as [...] Read more.
To address the issues of poor comfort and limited mobility associated with traditional ECG monitoring systems, this study developed a chest strap ECG monitoring system (CEMS) utilizing silver-coated polyamide yarn. This system can continuously capture high-quality ECG signals during daily activities such as walking and running, without restricting the user’s movement. Real-time data display and storage are enabled through a built-in Bluetooth module. Furthermore, leveraging these high-quality ECG signals, a classification model based on a fully connected neural network was constructed to evaluate exercise intensity by analyzing key ECG features. After 100 training epochs, the model achieved a classification accuracy of 98.7% for running intensity. The integration of this model with the CEMS enables effective tracking of ECG signals and accurate assessment of exercise intensity, offering a promising and practical solution for next-generation wearable signal monitoring systems. Full article
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18 pages, 2989 KiB  
Article
Interpretable Deep Learning for Pediatric Pneumonia Diagnosis Through Multi-Phase Feature Learning and Activation Patterns
by Petra Radočaj and Goran Martinović
Electronics 2025, 14(9), 1899; https://doi.org/10.3390/electronics14091899 - 7 May 2025
Viewed by 1164
Abstract
Pediatric pneumonia remains a critical global health challenge requiring accurate and interpretable diagnostic solutions. Although deep learning has shown potential for pneumonia recognition on chest X-ray images, gaps persist in understanding model interpretability and feature learning during training. We evaluated four convolutional neural [...] Read more.
Pediatric pneumonia remains a critical global health challenge requiring accurate and interpretable diagnostic solutions. Although deep learning has shown potential for pneumonia recognition on chest X-ray images, gaps persist in understanding model interpretability and feature learning during training. We evaluated four convolutional neural network (CNN) architectures, i.e., InceptionV3, InceptionResNetV2, DenseNet201, and MobileNetV2, using three approaches—standard convolution, multi-scale convolution, and strided convolution—all incorporating the Mish activation function. Among the tested models, InceptionResNetV2, with strided convolutions, demonstrated the best performance, achieving an accuracy of 0.9718. InceptionV3 also performed well using the same approach, with an accuracy of 0.9684. For DenseNet201 and MobileNetV2, the multi-scale convolution approach was more effective, with accuracies of 0.9676 and 0.9437, respectively. Gradient-weighted class activation mapping (Grad-CAM) visualizations provided critical insights, e.g., multi-scale convolutions identified diffuse viral pneumonia patterns across wider lung regions, while strided convolutions precisely highlighted localized bacterial consolidations, aligning with radiologists’ diagnostic priorities. These findings establish the following architectural guidelines: strided convolutions are suited to deep hierarchical CNNs, while multi-scale approaches optimize compact models. This research significantly advances the development of interpretable, high-performance diagnostic systems for pediatric pneumonia using chest X-rays, bridging the gap between computational innovation and clinical application. Full article
(This article belongs to the Section Computer Science & Engineering)
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12 pages, 359 KiB  
Article
Prevalence of Xpert MTB/RIF Ultra Trace Call Results and Associated Risk Factors During Active Tuberculosis Case Finding in Viet Nam: A Programmatic Evaluation
by Luong Van Dinh, Khoa Tu Tran, Andrew James Codlin, Luan Nguyen Quang Vo, Nga Thuy Thi Nguyen, Lan Phuong Nguyen, Rachel Forse, Han Thi Nguyen, Thi Minh Ha Dang, Lan Huu Nguyen, Hoa Binh Nguyen and Jacob Creswell
Diagnostics 2025, 15(8), 1006; https://doi.org/10.3390/diagnostics15081006 - 15 Apr 2025
Viewed by 1464
Abstract
Background: The Xpert MTB/RIF Ultra assay (Ultra) is a second-generation molecular diagnostic test for tuberculosis (TB). The “Trace Call” result was added as a semi-quantitative category for extremely low bacillary loads. The prevalence and interpretation of Trace Call results remains insufficiently elucidated in [...] Read more.
Background: The Xpert MTB/RIF Ultra assay (Ultra) is a second-generation molecular diagnostic test for tuberculosis (TB). The “Trace Call” result was added as a semi-quantitative category for extremely low bacillary loads. The prevalence and interpretation of Trace Call results remains insufficiently elucidated in the context of community-based active case finding (ACF). Methods: We organized 56 days of mobile chest X-ray (CXR) screening events in Ho Chi Minh City, Viet Nam, between October 2020 and March 2021. Participants were screened verbally and by CXR and tested by Ultra, if eligible. Persons with a Trace Call were re-tested on Ultra per national guidelines. qXRv3 computer-aided detection software was used for post hoc quality control of CXR interpretation. We calculated descriptive statistics and fitted mixed-effect multivariate regression models to identify factors associated with Trace Call results and confirmatory diagnosis. Results: A total of 16,698 people were screened by CXR to detect 185 Ultra-positive participants, including 142 persons with a confirmed TB diagnosis. Among Ultra-positive participants, 38.4% (71/185) had Trace Call results. Of these, 85.9% (61/71) were re-tested, and 45.9% (28/61) were bacteriologically-confirmed, comprising 19.7% (28/142) of the final number of confirmed diagnoses. Having a low qXR abnormality score (<0.5) (aOR = 4.97; 95%CI: [1.88, 13.14]; p = 0.001) and a history of TB within 5 recent years (aOR = 3.53; 95%CI: [1.69, 7.35]; p = 0.001) were associated with an initial Trace Call. Conclusions: The Trace Call can improve ACF detection, particularly in earlier stages of disease with limited pulmonary deterioration. However, participants with a history of TB had higher rates of Trace Call, reinforcing the need to interpret test results in this group with caution. Full article
(This article belongs to the Special Issue Tuberculosis Detection and Diagnosis 2025)
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19 pages, 7498 KiB  
Article
An Efficient Explainability of Deep Models on Medical Images
by Salim Khiat, Sidi Ahmed Mahmoudi, Sédrick Stassin, Lillia Boukerroui, Besma Senaï and Saïd Mahmoudi
Algorithms 2025, 18(4), 210; https://doi.org/10.3390/a18040210 - 9 Apr 2025
Viewed by 581
Abstract
Nowadays, Artificial Intelligence (AI) has revolutionized many fields and the medical field is no exception. Thanks to technological advancements and the emergence of Deep Learning (DL) techniques AI has brought new possibilities and significant improvements to medical practice. Despite the excellent results of [...] Read more.
Nowadays, Artificial Intelligence (AI) has revolutionized many fields and the medical field is no exception. Thanks to technological advancements and the emergence of Deep Learning (DL) techniques AI has brought new possibilities and significant improvements to medical practice. Despite the excellent results of DL models in terms of accuracy and performance, they remain black boxes as they do not provide meaningful insights into their internal functioning. This is where the field of Explainable AI (XAI) comes in, aiming to provide insights into the underlying workings of these black box models. In this present paper the visual explainability of deep models on chest radiography images are addressed. This research uses two datasets, the first on COVID-19, viral pneumonia, normality (healthy patients) and the second on pulmonary opacities. Initially the pretrained CNN models (VGG16, VGG19, ResNet50, MobileNetV2, Mixnet and EfficientNetB7) are used to classify chest radiography images. Then, the visual explainability methods (GradCAM, LIME, Vanilla Gradient, Gradient Integrated Gradient and SmoothGrad) are performed to understand and explain the decisions made by these models. The obtained results show that MobileNetV2 and VGG16 are the best models for the first and second datasets, respectively. As for the explainability methods, the results were subjected to doctors and were validated by calculating the mean opinion score. The doctors deemed GradCAM, LIME and Vanilla Gradient as the most effective methods, providing understandable and accurate explanations. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
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14 pages, 8180 KiB  
Case Report
A Dynamic Multimodality Imaging Assessment of Right Ventricular Thrombosis in a Middle-Aged Man with Lymphocytic Interstitial Pneumonia: The Additive Role of Tissue Doppler Imaging
by Andrea Sonaglioni, Alessandro Lucidi, Francesca Luisi, Antonella Caminati, Gian Luigi Nicolosi, Gaetana Anna Rispoli, Maurizio Zompatori, Michele Lombardo and Sergio Harari
J. Clin. Med. 2025, 14(6), 2035; https://doi.org/10.3390/jcm14062035 - 17 Mar 2025
Viewed by 668
Abstract
Background: Right ventricular thrombosis (RVT) is rarely detected in clinical practice. Depending on its aetiology, RVT may originate from a deep venous thrombosis (type A) or in situ (type B). Type A is characterized by increased mobility and frequent pulmonary embolization, whereas type [...] Read more.
Background: Right ventricular thrombosis (RVT) is rarely detected in clinical practice. Depending on its aetiology, RVT may originate from a deep venous thrombosis (type A) or in situ (type B). Type A is characterized by increased mobility and frequent pulmonary embolization, whereas type B is nonmobile and is associated with significant right ventricular (RV) dilatation and dysfunction. Methods: A type B RVT complicated by subsegmental pulmonary embolism (PE) was diagnosed in a 46-year-old man with acute-on-chronic respiratory failure secondary to acute exacerbation of interstitial lung disease. He underwent a multimodality imaging assessment of the RV mass that comprehensively incorporated TTE, TEE, contrast-enhanced chest CT, and LGE-CMR. Results: During the clinical course, a serial echocardiographic assessment of the RV mass allowed for a dynamic evaluation of its features and cardiac haemodynamics. Conventional TTE was implemented with colour tissue Doppler imaging (TDI) and pulsed wave (PW) TDI to improve the visualization of the RV mass and to objectively measure its mobility. The increased RVT mass peak antegrade velocity (>10 cm/s) was predictive of subsequent RVT fragmentation and PE. Conclusions: Colour TDI and PW-TDI may aid in the differential diagnosis of RV masses and may improve the prognostic risk stratification of patients with right-sided intracardiac masses. Full article
(This article belongs to the Special Issue What We See through Cardiac Imaging)
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11 pages, 1878 KiB  
Article
Typical Diagnostic Reference Levels of Radiation Exposure on Neonates Under 1 kg in Mobile Chest Imaging in Incubators
by Ioannis Antonakos, Matina Patsioti, Maria-Eleni Zachou, George Christopoulos and Efstathios P. Efstathopoulos
J. Imaging 2025, 11(3), 74; https://doi.org/10.3390/jimaging11030074 - 28 Feb 2025
Viewed by 1128
Abstract
The purpose of this study is to determine the typical diagnostic reference levels (DRLs) of radiation exposure values for chest radiographs in neonates (<1 kg) in mobile imaging at a University Hospital in Greece and compare these values with the existing DRL values [...] Read more.
The purpose of this study is to determine the typical diagnostic reference levels (DRLs) of radiation exposure values for chest radiographs in neonates (<1 kg) in mobile imaging at a University Hospital in Greece and compare these values with the existing DRL values from the literature. Patient and dosimetry data, including age, sex, weight, tube voltage (kV), tube current (mA), exposure time (s), exposure index of a digital detector (S), and dose area product (DAP) were obtained from a total of 80 chest radiography examinations performed on neonates (<1 kg and <30 days old). All examinations were performed in a single X-ray system, and all data (demographic and dosimetry data) were collected from the PACS of the hospital. Typical radiation exposure values were determined as the median value of DAP and ESD distribution. Afterward, these typical values were compared with DRL values from other countries. Three radiologists reviewed the images to evaluate image quality for dose optimization in neonatal chest radiography. From all examinations, the mean value and standard deviation of DAP was 0.13 ± 0.11 dGy·cm2 (range: 0.01–0.46 dGy·cm2), and ESD was measured at 11.55 ± 4.96 μGy (range: 4.01–30.4 μGy). The typical values in terms of DAP and ESD were estimated to be 0.08 dGy·cm2 and 9.87 μGy, respectively. The results show that the DAP value decreases as the exposure index increases. This study’s typical values were lower than the DRLs reported in the literature because our population had lower weight and age. From the subjective evaluation of image quality, it was revealed that the vast majority of radiographs (over 80%) met the criteria for being diagnostic as they received an excellent rating in terms of noise levels, contrast, and sharpness. This study contributes to the recording of typical dose values in a sensitive and rare category of patients (neonates weighing <1 kg) as well as information on the image quality of chest X-rays that were performed in this group. Full article
(This article belongs to the Special Issue Learning and Optimization for Medical Imaging)
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25 pages, 9497 KiB  
Article
Concealed Weapon Detection Using Thermal Cameras
by Juan D. Muñoz, Jesus Ruiz-Santaquiteria, Oscar Deniz and Gloria Bueno
J. Imaging 2025, 11(3), 72; https://doi.org/10.3390/jimaging11030072 - 26 Feb 2025
Cited by 2 | Viewed by 2917
Abstract
In an era where security concerns are ever-increasing, the need for advanced technology to detect visible and concealed weapons has become critical. This paper introduces a novel two-stage method for concealed handgun detection, leveraging thermal imaging and deep learning, offering a potential real-world [...] Read more.
In an era where security concerns are ever-increasing, the need for advanced technology to detect visible and concealed weapons has become critical. This paper introduces a novel two-stage method for concealed handgun detection, leveraging thermal imaging and deep learning, offering a potential real-world solution for law enforcement and surveillance applications. The approach first detects potential firearms at the frame level and subsequently verifies their association with a detected person, significantly reducing false positives and false negatives. Alarms are triggered only under specific conditions to ensure accurate and reliable detection, with precautionary alerts raised if no person is detected but a firearm is identified. Key contributions include a lightweight algorithm optimized for low-end embedded devices, making it suitable for wearable and mobile applications, and the creation of a tailored thermal dataset for controlled concealment scenarios. The system is implemented on a chest-worn Android smartphone with a miniature thermal camera, enabling hands-free operation. Experimental results validate the method’s effectiveness, achieving an mAP@50-95 of 64.52% on our dataset, improving state-of-the-art methods. By reducing false negatives and improving reliability, this study offers a scalable, practical solution for security applications. Full article
(This article belongs to the Special Issue Object Detection in Video Surveillance Systems)
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33 pages, 31157 KiB  
Article
A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
by Yongho Jeong, Taeuk Noh, Yonghak Lee, Seonjae Lee, Kwangil Choi, Sujin Jeong and Sunghwan Kim
Appl. Sci. 2025, 15(4), 2001; https://doi.org/10.3390/app15042001 - 14 Feb 2025
Cited by 1 | Viewed by 1662
Abstract
In this study, we propose a solution for automatically measuring body circumferences by utilizing the built-in LiDAR sensor in mobile devices. Traditional body measurement methods mainly rely on 2D images or manual measurements. This research, however, utilizes 3D depth information to enhance both [...] Read more.
In this study, we propose a solution for automatically measuring body circumferences by utilizing the built-in LiDAR sensor in mobile devices. Traditional body measurement methods mainly rely on 2D images or manual measurements. This research, however, utilizes 3D depth information to enhance both accuracy and efficiency. By employing HRNet-based keypoint detection and transfer learning through deep learning, the precise locations of body parts are identified and combined with depth maps to automatically calculate body circumferences. Experimental results demonstrate that the proposed method exhibits a relative error of up to 8% for major body parts such as waist, chest, hip, and buttock circumferences, with waist and buttock measurements recording low error rates below 4%. Although some models showed error rates of 7.8% and 7.4% in hip circumference measurements, this was attributed to the complexity of 3D structures and the challenges in selecting keypoint locations. Additionally, the use of depth map-based keypoint correction and regression analysis significantly improved accuracy compared to conventional 2D-based measurement methods. The real-time processing speed was also excellent, ensuring stable performance across various body types. Full article
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15 pages, 2443 KiB  
Perspective
Cardiac Arrest: Can Technology Be the Solution?
by Frédéric Lapostolle, Jean-Marc Agostinucci, Tomislav Petrovic and Anne-Laure Feral-Pierssens
J. Clin. Med. 2025, 14(3), 972; https://doi.org/10.3390/jcm14030972 - 3 Feb 2025
Viewed by 1176
Abstract
Out-of-hospital cardiac arrest (OHCA) mortality remains alarmingly high in most countries. The majority of pharmacological attempts to improve outcomes have failed. Randomized trials have shown limited survival benefits with vasopressin, fibrinolysis, amiodarone, or lidocaine. Even the benefits of adrenaline remain a matter of [...] Read more.
Out-of-hospital cardiac arrest (OHCA) mortality remains alarmingly high in most countries. The majority of pharmacological attempts to improve outcomes have failed. Randomized trials have shown limited survival benefits with vasopressin, fibrinolysis, amiodarone, or lidocaine. Even the benefits of adrenaline remain a matter of debate. In this context, relying on technology may seem appealing. However, technological strategies have also yielded disappointing results. This is exemplified by automated external chest compression devices. When first introduced, theoretical models, animal studies, and early clinical trials suggested they could improve survival. Yet, randomized trials failed to confirm this benefit. Similarly, to date, extracorporeal membrane oxygenation (ECMO), therapeutic hypothermia, and primary angioplasty have demonstrated inconsistent survival advantage. Other technological innovations continue to be explored, such as artificial intelligence to improve the diagnosis of cardiac arrest during emergency calls, mobile applications to dispatch citizen responders to patients in cardiac arrest, geolocation of defibrillators, and even the delivery of defibrillators via drones. Nevertheless, it is clear that the focus and investment should prioritize the initial links in the chain of survival: early alerting, chest compressions, and defibrillation. Significant improvements in these critical steps can be achieved through the education of children. Modern technological tools must be leveraged to enhance this training by incorporating gamification and democratizing access to education. These strategies hold the potential to fundamentally improve the management of cardiac arrest. Full article
(This article belongs to the Section Emergency Medicine)
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14 pages, 3771 KiB  
Article
Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables
by Mostafa Rezapour, Rachel B. Seymour, Suman Medda, Stephen H. Sims, Madhav A. Karunakar, Nahir Habet and Metin Nafi Gurcan
Bioengineering 2025, 12(1), 67; https://doi.org/10.3390/bioengineering12010067 - 14 Jan 2025
Viewed by 1323
Abstract
In a prospective study, we examined the recovery trajectory of patients with lower extremity fractures to better understand the healing process in the absence of complications. Using a chest-mounted inertial measurement unit (IMU) device for gait analysis and collecting patient-reported outcome measures, we [...] Read more.
In a prospective study, we examined the recovery trajectory of patients with lower extremity fractures to better understand the healing process in the absence of complications. Using a chest-mounted inertial measurement unit (IMU) device for gait analysis and collecting patient-reported outcome measures, we focused on 12 key gait variables, including Mean Leg Lift Acceleration, Stance Time, and Body Orientation. We employed a linear mixed model (LMM) to analyze these variables over time, incorporating both fixed and random effects to account for individual differences and the time since injury. This model also adjusted for varying intervals between assessments. Our study provided insights into gait recovery across different fracture types using data from 318 patients who experienced no complications or readmissions during their recovery. Through LMM analysis, we found that Tibia-Distal fractures demonstrated the fastest recovery, particularly in terms of mobility and strength. Tibia-Proximal fractures showed balanced improvements in both mobility and stability, suggesting that rehabilitation should target both strength and balance. Femur fractures exhibited varied recovery, with Diaphyseal fractures showing clear improvements in stability, while Distal fractures reflected gains in limb strength but with some variability in stability. To examine patients with readmissions, we conducted a Chi-squared test of independence to determine whether there was a relationship between fracture type and readmission rates, revealing a significant association (p < 0.001). Pelvis fractures had the highest readmission rates, while Tibia-Diaphyseal and Tibia-Distal fractures were more prone to infections, highlighting the need for enhanced infection control strategies. Femur fractures showed moderate readmission and infection rates, indicating a mixed risk profile. In conclusion, our findings emphasize the importance of fracture-specific rehabilitation strategies, focusing on infection prevention and individualized treatment plans to optimize recovery outcomes. Full article
(This article belongs to the Special Issue Technological Advances for Gait and Balance Assessment)
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31 pages, 5689 KiB  
Article
Reliability of an Inertial Measurement System Applied to the Technical Assessment of Forehand and Serve in Amateur Tennis Players
by Lucio Caprioli, Cristian Romagnoli, Francesca Campoli, Saeid Edriss, Elvira Padua, Vincenzo Bonaiuto and Giuseppe Annino
Bioengineering 2025, 12(1), 30; https://doi.org/10.3390/bioengineering12010030 - 2 Jan 2025
Cited by 3 | Viewed by 1686
Abstract
Traditional methods for evaluating tennis technique, such as visual observation and video analysis, are often subjective and time consuming. On the other hand, a quick and accurate assessment can provide immediate feedback to players and contribute to technical development, particularly in less experienced [...] Read more.
Traditional methods for evaluating tennis technique, such as visual observation and video analysis, are often subjective and time consuming. On the other hand, a quick and accurate assessment can provide immediate feedback to players and contribute to technical development, particularly in less experienced athletes. This study aims to validate the use of a single inertial measurement system to assess some relevant technical parameters of amateur players. Among other things, we attempt to search for significant correlations between the flexion extension and torsion of the torso and the lateral distance of the ball from the body at the instant of impact. This research involved a group of amateur players who performed a series of standardized gestures (forehands and serves) wearing a sensorized chest strap fitted with a wireless inertial unit. The collected data were processed to extract performance metrics. The percentage coefficient of variation for repeated measurements, Wilcoxon signed-rank test, and Spearman’s correlation were used to determine the system’s reliability. High reliability was found between sets of measurements in all of the investigated parameters. The statistical analysis showed moderate and strong correlations, suggesting possible applications in assessing and optimizing specific aspects of the technique, like the player’s distance to the ball in the forehand or the toss in the serve. The significant variations in technical execution among the subjects emphasized the need for tailored interventions through personalized feedback. Furthermore, the system allows for the highlighting of specific areas where intervention can be achieved in order to improve gesture execution. These results prompt us to consider this system’s effectiveness in developing an on-court mobile application. Full article
(This article belongs to the Special Issue Biomechanics of Physical Exercise)
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24 pages, 6316 KiB  
Systematic Review
Deep Learning Approaches for Chest Radiograph Interpretation: A Systematic Review
by Hammad Iqbal, Arshad Khan, Narayan Nepal, Faheem Khan and Yeon-Kug Moon
Electronics 2024, 13(23), 4688; https://doi.org/10.3390/electronics13234688 - 27 Nov 2024
Cited by 4 | Viewed by 3025
Abstract
Lung diseases are a major global health concern, with nearly 4 million deaths annually, according to the World Health Organization (WHO). Chest X-rays (CXR) are widely used as a cost-effective and efficient diagnostic tool by radiologists to detect conditions such as pneumonia, tuberculosis, [...] Read more.
Lung diseases are a major global health concern, with nearly 4 million deaths annually, according to the World Health Organization (WHO). Chest X-rays (CXR) are widely used as a cost-effective and efficient diagnostic tool by radiologists to detect conditions such as pneumonia, tuberculosis, COVID-19, and lung cancer. This review paper provides an overview of the current research on diagnosing lung diseases using CXR images and Artificial Intelligence (AI), without focusing on any specific disease. It examines different approaches employed by researchers to leverage CXR, an accessible diagnostic medium, for early lung disease detection. This review shortlisted 11 research papers addressing this problem through AI, exploring the datasets used and their sources. Results varied across studies: for lung cancer, Deep Convolutional Neural Network (DCNN) achieved 97.20% accuracy, while multiclass frameworks like ResNet152V2+Bi-GRU (gated reccurent unit) reached 79.78% and 93.38%, respectively. For COVID-19 detection, accuracy rates of 98% and 99.37% were achieved using EfficientNet and Parallel Convolutional Neural Network-Extreme Learning Machine (CNN-ELM). Additionally, studies on the CXR-14 dataset (14 classes) showed high accuracy, with MobileNet V2 reaching 94%. Other notable results include 73% accuracy with VDSNet, 98.05% with VGG19+CNN for three classes, and high accuracy in detecting pediatric pneumonia, lung opacity, pneumothorax, and tuberculosis. Full article
(This article belongs to the Special Issue Deep Learning for Computer Vision Application)
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