Medical & Biological Engineering & Computing最新文献

The Papanicolaou stain, consisting of five dyes, provides extensive color information essential for cervical cancer cytological screening. The visual observation of these colors is subjective and difficult to characterize. Direct RGB quantification is unreliable because RGB intensities vary with staining and imaging conditions. Stain unmixing offers a promising alternative by quantifying dye amounts. In previous work, multispectral imaging was utilized to estimate the dye amounts of Papanicolaou stain. However, its application to RGB images presents a challenge since the number of dyes exceeds the three RGB channels. This paper proposes a novel training-free Papanicolaou stain unmixing method for RGB images. This model enforces (i) nonnegativity, (ii) weighted nucleus sparsity for hematoxylin, and (iii) total variation smoothness, resulting in a convex optimization problem. Our method achieved excellent performance in stain quantification when validated against the results of multispectral imaging. We further used it to distinguish cells in lobular endocervical glandular hyperplasia (LEGH), a precancerous gastric-type adenocarcinoma lesion, from normal endocervical cells. Stain abundance features clearly separated the two groups, and a classifier based on stain abundance achieved 98.0% accuracy. By converting subjective color impressions into numerical markers, this technique highlights the strong promise of RGB-based stain unmixing for quantitative diagnosis.

Accurate posture assessment is essential for diagnosing and managing health issues related to postural disorders. Existing mobile applications rely on 2D imaging and analyzing reflective markers placed on anatomical landmarks of the human body without comprehensive view of body's posture. A mobile tool of 3D body reconstruction-based posture assessment is proposed in this paper. A smartphone is used to capture video circling around a static standing person in an approximate A-pose. Some specific multi-view body images using body orientation estimation are extracted from video. Then OpenPose and U2Net are employed to extract 2D joints and contours of each image. Camera poses are estimated using feature matching, and 3D joints are reconstructed from 2D joints and camera parameters. Multi-view projection contour consistency is used to iteratively optimize SMPL parameters for accurate 3D body reconstruction. From reconstructed 3D SMPL body, the required parameters for posture assessment including key 3D body points, joint angles, and spatial measurements, etc. can be easily obtained and calculated. Finally, assessment results are compared with artificial intelligence posture evaluation and correction system (APECS), and reliability is evaluated using Cohen's d and the Intraclass Correlation Coefficient (ICC). The proposed 3D body-based method achieved over 90% accuracy in identifying common postural abnormalities, such as uneven shoulders and forward head posture. The posture assessment results were in close agreement with existing method. 3D body reconstruction is demonstrated to be an effective method for posture assessment. Smartphone video-based posture assessment provides a user-friendly, visualized, abnormal posture screening tool.

Colorectal Cancer is one of the most prevalent cancers worldwide. One of the most critical factors for reducing the incidence of colorectal cancer is to increase the Adenoma Detection Rate (ADR), which is related to accurately detecting polyps during colonoscopy procedures. In this study, we developed a Convolutional Neural Network(CNN) architecture and utilised various techniques, including image processing and transfer learning. Our training dataset consisted of 1982 unique hand-labelled images extracted from 20 colonoscopy videos of different resolutions and 240 independent high-resolution colonoscopy images, all gathered by the Isfahan Gastroenterology and Hepatology Research Centre. Several experiments were conducted to assess the impact of CNN architectures on ADR. The experiments were designed to provide a fair evaluation of how the models would respond during a colonoscopy. The optimised CNN demonstrated excellent performance in polyp detection, achieving an Area Under the receiver operating characteristic Curve of 0.964 and an accuracy of 96.42%. The results were pretty consistent regarding different video resolutions and types of polyps. In addition, their result was compared concerning three colonoscopy specialists who were presented with multiple images for a reduced amount of time to simulate routine procedures. The CNN outperformed the average accuracy of the specialists by 5%. The proposed model demonstrates the potential to enhance and assist in the detection of adenomas and consequently contribute to higher prevention rates of colorectal cancer.

Surgical complications pose significant risks to patient safety and impose financial burdens, underscoring the need for reliable surgical skill training. Effective skill training requires accurate assessment. Conventional assessment methods are often subjective and labor-intensive. While motion metrics evaluate surgical performance, they provide limited insight into physiological mechanisms. This study assessed surgical proficiency through electromyography (EMG) during simulated laparoscopic tasks. Eighteen participants were recruited: five experts, five intermediates, and eight novices. EMG signals were recorded from Biceps Brachii, Triceps Brachii, Brachioradialis, Wrist Flexors, and Wrist Extensors of both arms. Root mean squared (RMS) values assessed muscle activity amplitude, mutual information (MI) quantified bimanual coordination, and instantaneous median frequency (IMDF) evaluated fatigue susceptibility. Higher skill levels, compared to lower levels, had significantly lower RMS EMG values in Biceps and Triceps, suggesting more relaxed muscle states. They exhibited significantly higher MI values, indicating superior bimanual coordination. Novices showed a significant decline in mean IMDF over trials, highlighting fatigue susceptibility, particularly in the Biceps and Triceps. These findings underscore EMG metrics' merit in objectively assessing surgical skill, providing insight into motor control, coordination, and fatigue. This multilevel physiological approach can inform training strategies and ergonomic interventions to improve surgical performance and reduce fatigue risk.

Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. This study presents a Graph Convolutional Neural Network combined with a Beta-Variational Autoencoder (GCN-β-VAE) framework for generating synthetic Abdominal Aortic Aneurysms (AAA). Using a small real-world dataset, our approach extracts key anatomical features and captures complex statistical relationships within a compact disentangled latent space. To address data limitations, low-impact data augmentation based on Procrustes analysis was employed, preserving anatomical integrity. The generation strategies, both deterministic and stochastic, manage to enhance data diversity while ensuring realism. Compared to PCA-based approaches, our model performs more robustly on unseen data by capturing complex, nonlinear anatomical variations. This enables more comprehensive clinical and statistical analyses than the original dataset alone. The resulting synthetic AAA dataset preserves patient privacy while providing a scalable foundation for medical research, device testing, and computational modeling.

Cerebrovascular regulation, driven by mechanisms such as cerebral autoregulation and the Cushing's reflex, plays a critical role in maintaining cerebral blood flow (CBF) adequate despite changes in arterial pressure (AP), since a dampening of CBF can lead to serious brain pathologies. This study investigates the causal and self-predictable dynamics of cerebrovascular interactions in patients undergoing coronary artery bypass graft surgery, before and after propofol general anaesthesia. The dynamics of the pressure-to-flow and flow-to-pressure links between mean arterial pressure (MAP) and mean cerebral blood velocity (MCBv) is assessed using time-domain and frequency-domain measures of Granger Causality (GC) and Granger Autonomy (GA). The results indicate that while time-domain indices remain stable, frequency-domain measures reveal variations in the very-low-frequency, low-frequency, and high-frequency (HF) bands. The increased spectral GC in the HF band may be related to the effect of mechanical ventilation during anaesthesia. Additionally, a reduction in self-dependency of MCBv in the HF band reflects weakened internal regulatory mechanisms post-anaesthesia. In conclusion, propofol-induced suppression of sympathetic control and the effects of mechanical respiration increase the dependence of cerebral blood flow on arterial pressure in specific bands of cerebrovascular interest. These findings underscore the importance of frequency-domain analysis in detecting subtle cerebrovascular dynamics that time-domain measures may overlook.