| CPC G06N 20/00 (2019.01) [A61B 34/10 (2016.02); A61B 34/20 (2016.02); G06F 18/2414 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G06T 7/70 (2017.01); G06T 7/75 (2017.01); G06V 10/82 (2022.01); G06V 30/19173 (2022.01); G06V 30/194 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/50 (2018.01); A61B 2034/102 (2016.02); A61B 2034/2051 (2016.02); A61B 2034/2065 (2016.02); A61B 2090/367 (2016.02); A61B 2090/376 (2016.02); A61F 2/01 (2013.01); A61F 2/0105 (2020.05); G06T 2207/10072 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10121 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30021 (2013.01)] | 30 Claims |

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1. A system for image-based detection of conditions for an object internal to a patient, the system comprising:
a database configured to store a trained model, the trained model being generated from one or more machine learning algorithms that are trained on training images of internal patient objects with annotated condition information, wherein the trained model is configured to be used to determine condition information from unannotated images of an object;
an imaging computer system that is configured to:
receive, from an imaging device, one or more images of the object internal to a patient, wherein the imaging device is configured to capture the one or more images of the object from a position external to the patient,
access the trained model from the database,
determine, based on applying the trained model to the one or more images of the object, current condition information for the object internal to the patient, and
provide the current condition information of the object internal to the patient; and
a display to monitor the object internal to the patient, the display being configured to output (i) the one or more images of the object as captured by the imaging device from the position external to the patient, and (ii) the current condition information for the object internal to the patient as determined based on application of the trained model to the one or more images.
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