Hang Shao

Background: Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons’ visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery. Methods and material: Forty-two microsurgeons from the Zhongshan Ophthalmic Center were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30 min conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool. Results: Accommodative lag decreased from 0.63 (0.18) diopters (D) to 0.55 (0.16) D (P=0.014), area under the log contrast sensitivity function increased from 1.49 (0.15) to 1.52 (0.14) (P=0.037), and heart rate variability decreased from 36.00 (13.54) milliseconds (ms) to 32.26 (12.35) ms (P=0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy (P>0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations [mean difference, 0.05 (95% CI: −1.17 to 1.08) points; P=0.95]. Subjective symptoms were quite mild after using both techniques. Conclusions: The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.

In this study, we introduce the U-Swin fusion model, an effective and efficient transformer-based architecture designed for the fusion of multi-focus microscope images. We utilized the Swin-Transformer with shifted window and path merging as the encoder for extracted hierarchical context features. Additionally, a Swin-Transformer-based decoder with patch expansion was designed to perform the un-sampling operation, generating the fully focused image. To enhance the performance of the feature decoder, the skip connections were applied to concatenate the hierarchical features from the encoder with the decoder up-sample features, like U-net. To facilitate comprehensive model training, we created a substantial dataset of multi-focus images, primarily derived from texture datasets. Our modulators demonstrated superior capability for multi-focus image fusion to achieve comparable or even better fusion images than the existing state-of-the-art image fusion algorithms and demonstrated adequate generalization ability for multi-focus microscope image fusion. Remarkably, for multi-focus microscope image fusion, the pure transformer-based U-Swin fusion model incorporating channel mix fusion rules delivers optimal performance compared with most existing end-to-end fusion models.