Automated analysis of pectoralis major thickness in pec-fly exercises: evolving from manual measurement to deep learning techniques.

This study addresses a limitation of prior research on pectoralis major (PMaj) thickness changes during the pectoralis fly exercise using a wearable ultrasound imaging setup. Although previous studies used manual measurement and subjective evaluation, it is important to acknowledge the subsequent limitations of automating widespread applications. We then employed a deep learning model for image segmentation and automated measurement to solve the problem and study the additional quantitative supplementary information that could be provided. Our results revealed increased PMaj thickness changes in the coronal plane within the probe detection region when real-time ultrasound imaging (RUSI) visual biofeedback was incorporated, regardless of load intensity (50% or 80% of one-repetition maximum). Additionally, participants showed uniform thickness changes in the PMaj in response to enhanced RUSI biofeedback. Notably, the differences in PMaj thickness changes between load intensities were reduced by RUSI biofeedback, suggesting altered muscle activation strategies. We identified the optimal measurement location for the maximal PMaj thickness close to the rib end and emphasized the lightweight applicability of our model for fitness training and muscle assessment. Further studies can refine load intensities, investigate diverse parameters, and employ different network models to enhance accuracy. This study contributes to our understanding of the effects of muscle physiology and exercise training.

Palm-Sized Wireless Transient Elastography System with Real-Time B-Mode Ultrasound Imaging Guidance: Toward Point-of-Care Liver Fibrosis Assessment.

Transient elastography (TE), recommended by the WHO, is an established method for characterizing liver fibrosis via liver stiffness measurement (LSM). However, technical barriers remain towards point-of-care application, as conventional TE requires wired connections, possesses a bulky size, and lacks adequate imaging guidance for precise liver localization. In this work, we report the design, phantom validation, and clinical evaluation of a palm-sized TE system that enables simultaneous B-mode imaging and LSM. The performance of this system was validated experimentally using tissue-equivalent reference phantoms (1.45-75 kPa). Comparative studies against other liver elastography techniques, including conventional TE and two-dimensional shear wave elastography (2D-SWE), were performed to evaluate its reliability and validity in adults with various chronic liver diseases. Intra- and inter-operator reliability of LSM were established by an elastography expert and a novice. A good agreement was observed between the Young's modulus reported by the phantom manufacturer and this system (bias: 1.1-8.6%). Among 121 patients, liver stiffness measured by this system and conventional TE were highly correlated (r = 0.975) and strongly agreed with each other (mean difference: -0.77 kPa). Inter-correlation of this system with conventional TE and 2D-SWE was observed. Excellent-to-good operator reliability was demonstrated in 60 patients (ICCs: 0.824-0.913). We demonstrated the feasibility of employing a fully integrated phased array probe for reliable and valid LSM, guided by real-time B-mode imaging of liver anatomy. This system represents the first technical advancement toward point-of-care liver fibrosis assessment. Its small footprint, along with B-mode guidance capability, improves examination efficiency and scales up screening for liver fibrosis.

Is histogram manipulation always beneficial when trying to improve model performance across devices? Experiments using a Meibomian gland segmentation model.

Meibomian gland dysfunction (MGD) is caused by abnormalities of the meibomian glands (MG) and is one of the causes of evaporative dry eye (DED). Precise MG segmentation is crucial for MGD-related DED diagnosis because the morphological parameters of MG are of importance. Deep learning has achieved state-of-the-art performance in medical image segmentation tasks, especially when training and test data come from the same distribution. But in practice, MG images can be acquired from different devices or hospitals. When testing image data from different distributions, deep learning models that have been trained on a specific distribution are prone to poor performance. Histogram specification (HS) has been reported as an effective method for contrast enhancement and improving model performance on images of different modalities. Additionally, contrast limited adaptive histogram equalization (CLAHE) will be used as a preprocessing method to enhance the contrast of MG images. In this study, we developed and evaluated the automatic segmentation method of the eyelid area and the MG area based on CNN and automatically calculated MG loss rate. This method is evaluated in the internal and external testing sets from two meibography devices. In addition, to assess whether HS and CLAHE improve segmentation results, we trained the network model using images from one device (internal testing set) and tested on images from another device (external testing set). High DSC (0.84 for MG region, 0.92 for eyelid region) for the internal test set was obtained, while for the external testing set, lower DSC (0.69-0.71 for MG region, 0.89-0.91 for eyelid region) was obtained. Also, HS and CLAHE were reported to have no statistical improvement in the segmentation results of MG in this experiment.