Abstract: Certain aspects of the present disclosure provide techniques for performing surgical ophthalmic procedures, such as cataract surgeries. An example method of determining one or more intraocular lens (IOL) parameters for an IOL to be used in a cataract surgery procedure includes generating, using a fused machine learning model, recommendations including one or more IOL parameters for the IOL to be used in the cataract surgery based, at least in part, on first predicted lens behavior for each of one or more IOLs by an emulated finite element analysis (EFEA) model and the second predicted lens behavior for each of the one or more IOLs by an IOL power calculator (IPC) machine learning model.

Abstract: A system and method for augmenting an original OCT (optical coherence tomography) image includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The system includes one or more learning modules selectively executable by the controller. The learning modules are trained by a training network with a training dataset having a plurality of training ultrasound bio-microscopy images and respective training OCT images. Execution of the instructions by the processor causes the controller to obtain the original OCT image, captured through an OCT device. The controller is configured to generate an augmented OCT image based in part on the original OCT image, by executing the (trained) learning modules. The augmented OCT image at least partially extends a peripheral portion of the original OCT image.

Abstract: A method for estimating biometric landmark dimensional measurements of a human eye includes, in a possible embodiment, receiving one or more images of the human eye via a host computer. In response to receiving the one or more images, the method includes generating a preliminary set of landmark point locations in the one or more images via the host computer using a deep-learning algorithm, and then refining the preliminary set of landmark point locations using a post-hoc processing routine of the host computer to thereby generate a final set of estimated landmark point locations. Additionally, the biometric landmark dimensional measurements are automatically generated via the host computer using the final set of estimated landmark point locations. A data set is then output that is inclusive of the set of estimated landmark point locations. A host computer that executes instructions from memory to perform the method.

Abstract: Disclosed are methods for sensing conditions of an electron microscope system and/or a specimen analyzed thereby. Also disclosed are sensor systems and electron microscope systems able to sense system conditions, and/or conditions of the specimen being analyzed by such systems. In one embodiment, a sparse dataset can be acquired from a random sub-sampling of the specimen by an electron beam probe of the electron microscope system. Instrument parameters, specimen characteristics, or both can be estimated from the sparse dataset.

Abstract: Disclosed are methods for sensing conditions of an electron microscope system and/or a specimen analyzed thereby. Also disclosed are sensor systems and electron microscope systems able to sense system conditions, and/or conditions of the specimen being analyzed by such systems. In one embodiment, a sparse dataset can be acquired from a random sub-sampling of the specimen by an electron beam probe of the electron microscope system. Instrument parameters, specimen characteristics, or both can be estimated from the sparse dataset.