Abstract: Disclosed in an integrated diagnostic imaging system (IDIS) designed to optimize the process of medical imaging and disease diagnosis. The IDIS can incorporate an intuitive disease selection interface that allows clinicians to select potential diseases of interest to screen for. Subsequently, a dynamic protocol selection algorithm can utilize a comprehensive imaging protocol database to determine the optimal imaging protocol corresponding to the selected diseases. The chosen protocols can be then executed by an imaging system, which can include different imaging modalities. Following the image acquisition, image analysis processes specific to the targeted diseases can process the images and identify pertinent features. The diagnostic results can be displayed in an intuitive and interactive results display, enhancing clinician interpretation and decision-making.

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: A handheld, portable devices with integrated artificial intelligence (AI) configured to assess a patient's body part to detect a disease and methods of operating such devices are disclosed. In some cases, a device can be a retina camera configured to assess a patient's retina and, by using an on-board AI retinal disease detection system, provide real-time analysis and diagnosis of the patient's retina. Easy and comfortable visualization of the patient's retina can be facilitated using such retina camera, which can be placed over the patient's eye, display the retina image on a high-resolution display, analyze a captured image by the on-board AI system, and provide determination of presence of a disease.

Abstract: A medical diagnostic instrument can include a housing with a mounting interface configured to support a plurality of imaging devices, each of the plurality of imaging devices configured to capture image data of a different anatomical region of a patient. The instrument can include an electronic processing circuitry, which can include a memory and a processor. The processor can be configured to, responsive to an attachment of an imaging device of the plurality of imaging devices to the mounting interface, retrieve from the memory and execute at least one machine learning model from a plurality of machine learning models configured to identify, based on image data, one or more diseases of the patient. The at least one machine learning model can be configured to identify one or more diseases of an anatomical region of the patient an image data of which the imaging device is configured to capture.