Abstract: Systems and methods for accurate, reliable, and comprehensive multi-disease retinal screening is disclosed. Leveraging a hierarchical deep learning architecture, a system can optimize feature extraction through a convolutional neural network (CNN) by first classifying images as normal or abnormal, and then discern the presence and severity of specific diseases using disease-specific CNN branches. The hierarchical deep learning architecture can promote feature disentanglement by associating each branch with a potentially unique set of parameters, enhancing disease-specific feature sensitivity. The system can further refine disease detection accuracy by implementing a probability classifier to exploit patterns of disease co-occurrence. A Monte Carlo Expectation-Maximization algorithm can optimize the parameters of the probability classifier, and a subsequent prediction calibration step can correct potential biases in the final predictions.

Abstract: A medical diagnostic system can assess quality of a representation of a body part determined based on a response of the body part to exposure to electromagnetic waves, process the representation with a disease detection machine learning model to determine a certainty measure for a presence of a disease, determine a quality score for the representation based on the quality of the representation and the certainty measure, and discard the at least one representation based on the quality score. Combining machine learning in conjunction with one another, such as, for quality assessment and disease detection, can provide for more accurate image quality analysis, lead to faster medical imaging, and reduce the need to retake images or entirely re-perform medical imaging. The system can be easier to use, be more robust and faster than other systems by reducing the need to retake images while maintaining performance of the system.

Abstract: Systems and methods that can perform real-time, artificial intelligence (AI) analysis of live retinal imaging on a medical diagnostics device are disclosed. In some cases, a retinal diagnostics instrument includes an imaging device configured to capture video data of an eye of a patient and an electronic processing circuitry configured to assess a quality of the video data of the eye of the patient, process the plurality of images of the eye with at least one machine learning model to determine a presence of at least one disease from a plurality of diseases that the at least one machine learning model has been trained to identify, and provide an indication of the presence of the at least one disease.

Abstract: A retinal diagnostics instrument can include an imaging device configured to capture image data of an eye of a patient and an electronic processing circuitry configured to execute a communication control method. The communication control method can include providing input data to and receiving output data from a plurality of modules configured to facilitate diagnosing a retina of the eye of the patient using the image data. The communication control method can further includes causing independent processing of input data by any first module of the plurality of modules and any second module of the plurality of modules, any second module being different than any first module, where processing of input data by any first module does not depend on directly providing data to or receiving data from any second module and vice versa.