Journal of Medical Engineering and Technology最新文献

Respiratory diseases are increasingly prevalent worldwide, often leading to critical conditions that require mechanical ventilation for life support. Proper management of these cases demands that clinicians be highly trained to respond effectively to various ventilatory manoeuvres during the recovery process. In this context, training tools for medical staff in mechanical ventilation become essential. Countries with emerging economies, such as Colombia, frequently face technological and economic limitations that restrict access to advanced medical training resources. As a result, the development of physical and virtual patient simulators presents a viable solution, as they can be designed using accessible technologies to support training in low-resource settings. This study presents SimVep, a patient simulator designed to emulate the physiological behaviour of obstructive and restrictive pulmonary conditions. The primary objective of SimVep is to enhance clinician training in mechanical ventilation, enabling healthcare professionals to acquire critical skills and improve patient outcomes in real-world clinical environments.

Attention deficit hyperactivity disorder (ADHD) is one of the children's most common neurodevelopmental conditions. ADHD diagnosis is based on evaluating inattention, hyperactivity, and impulsivity symptoms that interfere with or reduce daily functioning. Although electroencephalography (EEG) tests are used for ADHD diagnosis, they are generally considered a complement to clinical evaluation. This paper proposes an approach to classify EEG records of children with ADHD and control cases. We identified and extracted relevant features from EEG signals of 47 children (22 diagnosed with ADHD and 25 controls) and evaluated machine learning techniques for classification. We used the 2-tone oddball paradigm to elicit the subjects' auditory event-related potentials (ERP), and we recorded EEG signals with a portable headset for approximately five minutes. In the feature extraction stage, we included measures from cognitive evoked potentials, frequency bands power, chaos quantification, and bispectral analysis, in addition to the age of the children and the number of high-pitched tones the children counted during the test. The SVM and Trees algorithms obtained the best performance for 86.36% accuracy and 95.45% sensitivity. These findings demonstrate the potential of portable EEG-based systems to complement standard clinical assessments, offering an objective, time-efficient, and accessible approach to support early ADHD diagnosis. Achieving high accuracy and sensitivity in classification is critical to reducing the risk of misdiagnosis and ensuring timely intervention, ultimately improving patient outcomes.

Background: Recently, dynamic total-body positron emission tomography (PET) imaging has become possible due to new scanner devices. However, there is still little research systematically evaluating clustering algorithms for processing of dynamic total-body PET images.

Materials and methods: Here, we compare the performance of 15 unsupervised clustering methods, including K-means either by itself or after principal component analysis (PCA) or independent component analysis (ICA), Gaussian mixture model (GMM), fuzzy c-means (FCM), agglomerative clustering, spectral clustering, and several newer clustering algorithms, for classifying time activity curves (TACs) in dynamic PET images. We use dynamic total-body ¹⁵O-water PET images of 30 patients. To evaluate the clustering algorithms in a quantitative way, we use them to classify 5000 TACs from each image based on whether the curve is taken from brain, right heart ventricle, right kidney, lower right lung lobe, or urinary bladder.

Results: According to our results, the best methods are GMM, FCM, and ICA combined with mini batch K-means, which classified the TACs with a median accuracies of 89%, 83%, and 81%, respectively, in a processing time of half a second or less.

Conclusion: GMM, FCM, and ICA with mini batch K-means show promise for dynamic total-body PET analysis.

Cardiovascular diseases (CVDs) significantly impact athletes, impacting the heart and blood vessels. This article introduces a novel method to assess CVD in athletes through an artificial neural network (ANN). The model utilises the mutual learning-based artificial bee colony (ML-ABC) algorithm to set initial weights and proximal policy optimisation (PPO) to address imbalanced classification. ML-ABC uses mutual learning to enhance the learning process by updating the positions of the food sources with respect to the best fitness outcomes of two randomly selected individuals. PPO makes updates in the ANN stable and efficient to improve the model's reliability. Our approach formulates the classification problem as a series of decision-making processes, rewarding every classification act with higher rewards for correctly identifying the instances of the minority class, hence handling class imbalance. We evaluated the model's performance on a diversified medical dataset including 26,002 athletes who were examined within the Polyclinic for Occupational Health and Sports in Zagreb, further validated with NCAA and NHANES datasets to verify generalisability. Our findings indicate that our model outperforms existing models with accuracies of 0.88, 0.86 and 0.82 for the respective datasets. These results enhance clinical model application and advance cardiovascular disorder detection and methodologies.

Uroflowmetry plays an important role in the investigation of patients with lower urinary tract symptoms. We were required to assess a newly developed uroflowmeter. We thus aimed to produce a standardised protocol to test the accuracy and filtering of any new uroflowmeter. The accuracy of a newly developed uroflowmeter (Minze Uroflow^®) was validated using a constant flow bottle and a cylindrical flow column. Two other machines were also tested alongside. We also assessed filtering by reproducing common artefacts in the laboratory. Finally, a questionnaire was constructed to assess the usability of the uroflowmeter by clinicians during a normal hospital flow clinic. A protocol to test new uroflowmeters was written and assessed. The protocol showed the following results for the tested uroflowmeters: a simple bench test using a constant flow bottle and cylindric column showed that the uroflow parameters (Q_max and V_void) were within the claimed accuracy range and ICS recommendations. The processing of the flow data by the systems effectively filtered noise, and the flow rate decline over the whole measurement range, as produced by the cylindrical flow column, was smooth and linear. Usability was assessed by clinicians in their routine clinical practice. The proposed tests meet the requirements of the ICS guidelines. We have designed a protocol which can be used by clinicians and researchers to validate the accuracy of their uroflowmeters, evaluate new models and ensure clinical usefulness.

This review article delves into the cutting-edge realm of 3D printing and its impact on the fabrication of customised orthodontic retainers, which is an essential utility in the prevention of relapse post orthodontic treatment. This review evaluates the use of biocompatible materials and provides insight into future perspectives and improvements in this field. It highlights the potential of data collecting method and 3D printing to improve orthodontic retainers' fabrication and emphasises the importance of using biocompatible materials for patient safety and efficacy. It also explains cytotoxic qualities of retainer fabrication materials, which are vital for safeguarding the oral health of the patient. The evaluation procedure enables the early diagnosis and correction of any potential difficulties, such as maladjustment or inappropriate fit, allowing for a more effective treatment. It illustrates the breakthroughs and innovations in the field of orthodontics, the advantages of 3D printing over conventional methods, as well as the advantages and disadvantages of various fabrication method. Incorporating 3D printing and review into the production of orthodontic retainers enhances the overall effectiveness and efficiency of patient treatment.