Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography.

Minimally-invasive microsurgery has resulted in improved outcomes for patients. However, operating through a microscope limits depth perception and fixes the visual perspective, which result in a steep learning curve to achieve microsurgical proficiency. We introduce a surgical imaging system employing four-dimensional (live volumetric imaging through time) microscope-integrated optical coherence tomography (4D MIOCT) capable of imaging at up to 10 volumes per second to visualize human microsurgery. A custom stereoscopic heads-up display provides real-time interactive volumetric feedback to the surgeon. We report that 4D MIOCT enhanced suturing accuracy and control of instrument positioning in mock surgical trials involving 17 ophthalmic surgeons. Additionally, 4D MIOCT imaging was performed in 48 human eye surgeries and was demonstrated to successfully visualize the pathology of interest in concordance with preoperative diagnosis in 93% of retinal surgeries and the surgical site of interest in 100% of anterior segment surgeries. In vivo 4D MIOCT imaging revealed sub-surface pathologic structures and instrument-induced lesions that were invisible through the operating microscope during standard surgical maneuvers. In select cases, 4D MIOCT guidance was necessary to resolve such lesions and prevent post-operative complications. Our novel surgical visualization platform achieves surgeon-interactive 4D visualization of live surgery which could expand the surgeon's capabilities.

Glucose variability and inner retinal sensory neuropathy in persons with type 1 diabetes mellitus.

PurposeTo quantify early neuroretinal alterations in patients with type 1 diabetes mellitus (T1DM) and to assess whether glycemic variability contributes to alterations in neuroretinal structure or function.MethodsThirty patients with T1DM and 51 controls underwent comprehensive ophthalmic examination and assessment of retinal function or structure with frequency doubling perimetry (FDP), contrast sensitivity, dark adaptation, fundus photography, and optical coherence tomography (OCT). Diabetic participants wore a subcutaneous continuous glucose monitor for 5 days, from which makers of glycemic variability including the low blood glucose index (LGBI) and area under the curve (AUC) for hypoglycemia were derived.ResultsSixteen patients had no diabetic retinopathy (DR), and 14 had mild or moderate DR. Log contrast sensitivity for the DM group was significantly reduced (mean±SD=1.63±0.06) compared with controls (1.77±0.13, P<0.001). OCT analysis revealed that the inner temporal inner nuclear layer (INL) was thinner in patients with T1DM (34.9±2.8 µm) compared with controls (36.5±2.9 µm) (P=0.023), although this effect lost statistical significance after application of the Bonferroni correction for multiple comparisons. Both markers of glycemic variability, the AUC for hypoglycemia (R=-0.458, P=0.006) and LGBI (R=-0.473, P=0.004), were negatively correlated with inner temporal INL thickness.ConclusionsPatients with T1DM and no to moderate DR exhibit alterations in inner retinal structure and function. Increased glycemic variability correlates with retinal thinning on OCT imaging, suggesting that fluctuations in blood glucose may contribute to neurodegeneration.

Deblurring using regularized locally adaptive kernel regression.

Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation [1]. In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally performed in two sequential steps, namely denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior that generalizes some of the most popular regularization techniques in the literature. Experimental results demonstrate the effectiveness of our method.

Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks.

Artificial neural networks are intelligent systems that have been successfully used for prediction in different medical fields. In this study, efficiency of neural networks for prediction of lupus nephritis in patients with systemic lupus erythematosus (SLE) was compared with a logistic regression model and clinicians' diagnosis. Overall accuracy, sensitivity and specificity of the optimal neural network were 68.69, 73.77 and 62.96%, respectively. Overall accuracy of neural network was greater than the other two methods (P-value < 0.05). The neural network was more specific in predicting lupus nephritis (P-value < 0.01), but there was no significant difference between sensitivities of the three methods. Sensitivities of all three methods were greater than their specificities. We concluded that neural networks are efficient in predicting lupus nephritis in SLE patients.