Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Methods and apparatus for the application of reinforcement learning to animal medical diagnostics. In one embodiment, a system is disclosed that utilizes two (2) RL agents that are arranged sequentially and that are optimized independently from one another. The first RL agent is an assessment RL agent which takes as input from one or more of: outputs from classification artificial intelligence (AI) engines; outputs from subjective biological data storage devices, and outputs from objective biological data storage devices. Using these input(s), the assessment RL agent outputs a set of assessments which evaluate a set of conditions associated with an animal. This set of assessments is then provided as input to a second RL agent known-as a plan RL agent which determines a set of treatment recommendations and diagnostics based off this determined set of assessments. Methods and computer-readable media are also disclosed.

Abstract: Methods and apparatus for the application of reinforcement learning to animal medical diagnostics. In one embodiment, a system is disclosed that utilizes two (2) RL agents that are arranged sequentially and that are optimized independently from one another. The first RL agent is an assessment RL agent which takes as input from one or more of: outputs from classification artificial intelligence (AI) engines; outputs from subjective biological data storage devices, and outputs from objective biological data storage devices. Using these input(s), the assessment RL agent outputs a set of assessments which evaluate a set of conditions associated with an animal. This set of assessments is then provided as input to a second RL agent known-as a plan RL agent which determines a set of treatment recommendations and diagnostics based off this determined set of assessments. Methods and computer-readable media are also disclosed.

Abstract: Methods and apparatus for the application of reinforcement learning to animal medical diagnostics. In one embodiment, a system is disclosed that utilizes two (2) RL agents that are arranged sequentially and that are optimized independently from one another. The first RL agent is an assessment RL agent which takes as input from one or more of: outputs from classification artificial intelligence (AI) engines; outputs from subjective biological data storage devices, and outputs from objective biological data storage devices. Using these input(s), the assessment RL agent outputs a set of assessments which evaluate a set of conditions associated with an animal. This set of assessments is then provided as input to a second RL agent known-as a plan RL agent which determines a set of treatment recommendations and diagnostics based off this determined set of assessments. Methods and computer-readable media are also disclosed.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Provided are methods for screening a subject for cancer. The methods involve obtaining a blood sample from the subject and determining a level of Bridging Integrator 1 (BIN1) isoforms comprising exon 12a in the sample. Optionally, the method involves determining a level of 12a+/13? BIN isoform (comprising exon 12a but lacking exon 13) in the sample. An elevated level of 12a+(e.g., 12a+/13?) BIN1 isoforms in the blood sample indicates the subject has cancer. Also provided are methods for determining efficacy of a cancer therapy in a subject and methods of treating cancer. Isolated antibodies that selectively bind human 12a+ BIN1 are also provided as well as kits for determining 12a+/13? BIN1 isoforms.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: Provided are methods of determining if a subject has a cardiac disorder or is at risk for developing a cardiac disorder.

Abstract: Provided are methods for screening a subject for cancer. The methods involve obtaining a blood sample from the subject and determining a level of Bridging Integrator 1 (BIN1) isoforms comprising exon 12a in the sample. Optionally, the method involves determining a level of 12a+/13? BIN isoform (comprising exon 12a but lacking exon 13) in the sample. An elevated level of 12a+(e.g., 12a+/13?) BIN1 isoforms in the blood sample indicates the subject has cancer. Also provided are methods for determining efficacy of a cancer therapy in a subject and methods of treating cancer. Isolated antibodies that selectively bind human 12a+ BIN1 are also provided as well as kits for determining 12a+/13? BIN1 isoforms.

Abstract: Provided are methods for determining skeletal muscle mass in subject. Also provided are methods for diagnosing a neurological condition or disease or a condition or disease associated with reduced skeletal muscle mass in a subject. Further provided are purified antibodies that bind specifically to a BIN1 polypeptide that is expressed specifically in skeletal muscle.

Abstract: Provided are methods of determining if a subject has a cardiac disorder or is at risk for developing a cardiac disorder.

Abstract: Provided are methods for screening a subject for cancer. The methods involve obtaining a blood sample from the subject and determining a level of Bridging Integrator 1 (BIN1) isoforms comprising exon 12a in the sample. Optionally, the method involves determining a level of 12a+/13? BIN isoform (comprising exon 12a but lacking exon 13) in the sample. An elevated level of 12a+ (e.g., 12a+/13?) BIN1 isoforms in the blood sample indicates the subject has cancer. Also provided are methods for determining efficacy of a cancer therapy in a subject and methods of treating cancer. Isolated antibodies that selectively bind human 12a+ BIN1 are also provided as well as kits for determining 12a+/13? BIN1 isoforms.

Abstract: Provided are methods for screening a subject for cancer. The methods involve obtaining a blood sample from the subject and determining a level of Bridging Integrator 1 (BIND isoforms comprising exon 12a in the sample. Optionally, the method involves determining a level of 12a+/13? BIN isoform (comprising exon 12a but lacking exon 13) in the sample. An elevated level of 12a+ (e.g., 12a+/13?) BIN1 isoforms in the blood sample indicates the subject has cancer. Also provided are methods for determining efficacy of a cancer therapy in a subject and methods of treating cancer. Isolated antibodies that selectively bind human 12a+ BIN1 are also provided as well as kits for determining 12a+/13? BIN1 isoforms.

Abstract: Provided are methods for determining skeletal muscle mass in subject. Also provided are methods for diagnosing a neurological condition or disease or a condition or disease associated with reduced skeletal muscle mass in a subject. Further provided are purified antibodies that bind specifically to a BIN1 polypeptide that is expressed specifically in skeletal muscle.