Discrimination of vascular proliferation in eyelid surgery using multimodal hyperspectral imaging technology

Vascular proliferation is a common skin reaction following eyelid surgery, and early diagnosis is crucial for improving treatment outcomes. This study proposes a multimodal framework for the diagnosis of vascular proliferation in eyelid tissues, in which hyperspectral images provide spectral-spatial information related to microvascular changes, while corresponding pathological slice images provide complementary microscopic morphological information. These two modalities were jointly analyzed using the HyperT-net deep learning model.

Hyperspectral data ranging from 400 to 1000 nm and corresponding pathological slice images were collected for model development and evaluation. A combination of one-dimensional convolutional networks and Transformer architecture was employed to build the HyperT-net model for data analysis.

The proposed approach effectively captures subtle changes in skin microvasculature, providing a non-invasive and precise tool for skin condition assessment. Compared to existing methods, our approach demonstrates superior accuracy and sensitivity, particularly in the early detection of vascular proliferation. Additionally, ablation experiments reveal that each component of the model plays a critical role in enhancing its performance.

This research introduces a novel technological pathway for diagnosing vascular proliferation in the field of medical aesthetics and lays the foundation for the future promotion of related applications.