Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema.

Anti-vascular endothelial growth factor (VEGF) agents are widely regarded as the first line of therapy for diabetic macular edema (DME) but are not universally effective. An automatic method that can predict whether a patient is likely to respond to anti-VEGF therapy can avoid unnecessary trial and error treatment strategies and promote the selection of more effective first-line therapies. The objective of this study is to automatically predict the efficacy of anti-VEGF treatment of DME in individual patients based on optical coherence tomography (OCT) images. We performed a retrospective study of 127 subjects treated for DME with three consecutive injections of anti-VEGF agents. Patients' retinas were imaged using spectral-domain OCT (SD-OCT) before and after anti-VEGF therapy, and the total retinal thicknesses before and after treatment were extracted from OCT B-scans. A novel deep convolutional neural network was designed and evaluated using pre-treatment OCT scans as input and differential retinal thickness as output, with 5-fold cross-validation. The group of patients responsive to anti-VEGF treatment was defined as those with at least a 10% reduction in retinal thickness following treatment. The predictive performance of the system was evaluated by calculating the precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The algorithm achieved an average AUC of 0.866 in discriminating responsive from non-responsive patients, with an average precision, sensitivity, and specificity of 85.5%, 80.1%, and 85.0%, respectively. Classification precision was significantly higher when differentiating between very responsive and very unresponsive patients. The proposed automatic algorithm accurately predicts the response to anti-VEGF treatment in DME patients based on OCT images. This pilot study is a critical step toward using non-invasive imaging and automated analysis to select the most effective therapy for a patient's specific disease condition.

Interface design for CMOS-integrated Electrochemical Impedance Spectroscopy (EIS) biosensors.

Electrochemical Impedance Spectroscopy (EIS) is a powerful electrochemical technique to detect biomolecules. EIS has the potential of carrying out label-free and real-time detection, and in addition, can be easily implemented using electronic integrated circuits (ICs) that are built through standard semiconductor fabrication processes. This paper focuses on the various design and optimization aspects of EIS ICs, particularly the bio-to-semiconductor interface design. We discuss, in detail, considerations such as the choice of the electrode surface in view of IC manufacturing, surface linkers, and development of optimal bio-molecular detection protocols. We also report experimental results, using both macro- and micro-electrodes to demonstrate the design trade-offs and ultimately validate our optimization procedures.