-
公开(公告)号：US20230389827A1
公开(公告)日：2023-12-07
申请号：US18236287
申请日：2023-08-21
Applicant: Digital Diagnostics Inc.
Inventor： Michael D. Abramoff , Ryan Amelon
CPC classification number: A61B5/121 , A61B5/7275 , A61B5/0066 , G16H10/60 , A61B5/4842 , G16H50/20 , G16H50/30 , G16H50/70 , A61B1/227
Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.
-
Patent Agency Ranking
公开(公告)号：US20230419485A1
公开(公告)日：2023-12-28
申请号：US18367384
申请日：2023-09-12
Applicant: Digital Diagnostics Inc.
Inventor： Meindert Niemeijer , Ryan Amelon , Warren Clarida , Michael D. Abramoff
CPC classification number: G06T7/0012 , G06N3/08 , G06V10/454 , G06F18/2148 , G06N3/045 , G06V10/82 , G06T2207/20084 , G06T2207/20081 , G06T2207/30041
Abstract: Provide are systems methods and devices for diagnosing disease in medical images. In certain aspects, disclosed is a method for training a neural network to detect features in a retinal image including the steps of: a) extracting one or more features images from a Train_0 set, a Test_0 set, a Train_1 set and a Test_1 set; b) combining and randomizing the feature images from Train_0 and Train_1 into a Training data set; c) combining and randomizing the feature images from Test_0 and Test_1 into a testing dataset; d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and g) repeating steps d)-g) until an objective performance threshold is reached.
-
公开(公告)号：US11786148B2
公开(公告)日：2023-10-17
申请号：US16529685
申请日：2019-08-01
Applicant: Digital Diagnostics Inc.
Inventor： Michael D. Abramoff , Ryan Amelon
CPC classification number: A61B5/121 , A61B5/0066 , A61B5/4842 , A61B5/7275 , G16H10/60 , G16H50/20 , G16H50/30 , G16H50/70 , A61B1/227
Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.
-
公开(公告)号：US12011261B2
公开(公告)日：2024-06-18
申请号：US18236287
申请日：2023-08-21
Applicant: Digital Diagnostics Inc.
Inventor： Michael D. Abramoff , Ryan Amelon
CPC classification number: A61B5/121 , A61B5/0066 , A61B5/4842 , A61B5/7275 , G16H10/60 , G16H50/20 , G16H50/30 , G16H50/70 , A61B1/227
Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.
-
公开(公告)号：US20240293045A1
公开(公告)日：2024-09-05
申请号：US18665415
申请日：2024-05-15
Applicant: Digital Diagnostics Inc.
Inventor： Michael D. Abramoff , Ryan Amelon
CPC classification number: A61B5/121 , A61B5/0066 , A61B5/4842 , A61B5/7275 , G16H10/60 , G16H50/20 , G16H50/30 , G16H50/70 , A61B1/227
Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.
-
公开(公告)号：US11790523B2
公开(公告)日：2023-10-17
申请号：US16175318
申请日：2018-10-30
Applicant: Digital Diagnostics Inc.
Inventor： Meindert Niemeijer , Ryan Amelon , Warren Clarida , Michael D. Abramoff
CPC classification number: G06T7/0012 , G06F18/2148 , G06N3/045 , G06N3/08 , G06V10/454 , G06V10/82 , G06T2207/20081 , G06T2207/20084 , G06T2207/30041
Abstract: A device receives an input image of a portion of a patient's body, and applies the input image to a feature extraction model, the feature extraction model comprising a trained machine learning model that is configured to generate an output that comprises, for each respective location of a plurality of locations in the input image, an indication that the input image contains an object of interest that is indicative of a presence of a disease state at the respective location. The device applies the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on the output of the feature extraction model. The device outputs the determined diagnosis of a disease condition in the patient obtained from the diagnostic model.
-
-
-
-
-
Search Results
6
records
(took 0.029 seconds)
