Continuous monitoring of blood pressure (BP) waveform is challenging in clinical applications due to the invasive nature of traditional techniques. As a result, there is a growing focus on the estimation of continuous BP waveforms from photoplethysmography (PPG) signals obtained through affordable wearable Internet of Medical Things (IoMT) devices. To address this demand, we introduce DiffCNBP (Diffusion for Continuous Non-invasive BP), a lightweight model that employs a joint optimization approach incorporating sequence learning, diffusion modeling, and conditional embedding. The sequence learning module comprises stacked Transformer encoders to capture the temporal information of the PPG signals. These dynamically related features are then fed into a lightweight diffusion module based on structured state space sequence encoders to learn detailed variation features associated with vascular dynamics. Additionally, the conditional embedding module introduces constraints to incorporate physiologically specific prior information for model estimation, thereby enhancing the fidelity of the estimated BP waveform. Our proposed method was validated using subject-wise five-fold cross-validation on a multi-source database of 1,415 subjects. This database included data from intensive care unit IoMT applications, where finger-based PPG sensors were employed alongside invasive BP sensors. Experimental results demonstrate that DiffCNBP outperforms other the state-of-the-art methods with an average root mean square error of 4.36 mmHg for waveform estimation. The mean error ± standard deviation error of systolic and diastolic BP was 0.67 ± 4.29 mmHg and 0.37 ± 2.46 mmHg, respectively, meeting the clinical standards. Furthermore, we demonstrated the robust long-term trend-tracking ability of DiffCNBP on resource-constrained devices, indicating its potential IoMT deployment in clinical settings.

Retinopathy, a prevalent disease causing visual impairment and sometimes blindness, affects many individuals in the population. Early detection and treatment of the disease can be facilitated by monitoring the retina using fundus imaging. Nonetheless, the limited availability of fundus images and the imbalanced datasets warrant the development of more precise and efficient algorithms to enhance diagnostic performance. This study presents a novel online knowledge distillation framework, called CLRD, which employs a collaborative learning approach for detecting retinopathy. By combining student models with varying scales and architectures, the CLRD framework extracts crucial pathological information from fundus images. The transfer of knowledge is accomplished by developing distortion information particular to fundus images, thereby enhancing model invariance. Our selection of student models includes the Transformer-based BEiT and the CNN-based ConvNeXt, which achieve accuracies of 98.77% and 96.88%, respectively. Furthermore, the proposed method has 5.69–23.13%, 5.37–23.73%, 5.74–23.17%, 11.24–45.21%, and 5.87–24.96% higher accuracy, precision, recall, specificity, and F1 score, respectively, compared to the advanced visual model. The results of our study indicate that the CLRD framework can effectively minimize generalization errors without compromising independent predictions made by student models, offering novel directions for further investigations into detecting retinopathy.

One of the early manifestations of systemic atherosclerosis, which leads to blood circulation issues, is the enhanced arterial light reflex (EALR). Fundus images are commonly used for regular screening purposes to intervene and assess the severity of systemic atherosclerosis in a timely manner. However, there is a lack of automated methods that can meet the demands of large-scale population screening. Therefore, this study introduces a novel cross-scale transformer-based multi-instance learning method, named MIL-CT, for the detection of early arterial lesions (e.g., EALR) in fundus images. MIL-CT utilizes the cross-scale vision transformer to extract retinal features in a multi-granularity perceptual domain. It incorporates a multi-head cross-scale attention fusion module to enhance global perceptual capability and feature representation. By integrating information from different scales and minimizing information loss, the method significantly improves the performance of the EALR detection task. Furthermore, a multi-instance learning module is implemented to enable the model to better comprehend local details and features in fundus images, facilitating the classification of patch tokens related to retinal lesions. To effectively learn the features associated with retinal lesions, we utilize weights pre-trained on a large fundus image Kaggle dataset. Our validation and comparison experiments conducted on our collected EALR dataset demonstrate the effectiveness of the MIL-CT method in reducing generalization errors while maintaining efficient attention to retinal vascular details. Moreover, the method surpasses existing models in EALR detection, achieving an accuracy, precision, sensitivity, specificity, and F1 score of 97.62%, 97.63%, 97.05%, 96.48%, and 97.62%, respectively. These results exhibit the significant enhancement in diagnostic accuracy of fundus images brought about by the MIL-CT method. Thus, it holds potential for various applications, particularly in the early screening of cardiovascular diseases such as hypertension and atherosclerosis.