Implant Image Cohort Analysis for Faster Position Interpretation
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Solution Overview
Problem
The manual interpretation of medical images containing implant devices in the human body is a time-consuming and error-prone process, despite advancements in imaging technology.
Innovation Solution
An apparatus and method utilizing a processor and memory to receive and classify historical subject data, including image and textual data, into implant cohorts, and employ a machine-learning model to generate output data related to the position of implant devices, leveraging a repository of images through a machine-learning model like ConVIRT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of medical images is used, then diagnostic accuracy can be maintained through expert analysis, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent introduces an intermediary system consisting of a processor and machine learning model that acts as a mediator between the medical images and the final diagnostic interpretation. This intermediary automatically processes images to identify implant devices and their positions, reducing the time burden on manual interpretation while maintaining diagnostic accuracy through automated analysis.
Solution Approach 2:
The patent replaces the mechanical system of manual image interpretation with an automated computational system. The processor and machine learning model substitute for human experts in the actual image analysis task, performing automated detection and classification of implant devices, thereby eliminating the time-consuming nature of manual review while preserving diagnostic precision.
2Adaptability or versatility
If manual interpretation is used, then flexibility in handling diverse implant types is maintained, but the process becomes error-prone and inconsistent
Solution Approach 1:
The patent implements a universal machine learning model that can handle multiple types of implant devices through a single system. The model is trained on diverse historical subject data encompassing various implant types, manufacturers, and anatomical locations, enabling it to automatically adapt to and accurately identify different implant categories without requiring separate manual interpretation protocols for each type.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model is trained on historical subject data that includes both image data and corresponding implant information. This feedback loop allows the model to continuously improve its accuracy and consistency in identifying implant devices, reducing errors while maintaining the ability to handle diverse implant types through learned patterns from extensive training data.
Data Source
AI summary
An apparatus method for leveraging a repository of images containing implant devices in a human body are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a plurality of sets of historical subject data, classify the plurality of sets of historical subject data into one or more implant cohorts, receive an inquiry datum from a user, wherein the inquiry datum includes current subject data and generate an output datum as a function of the inquiry datum using an implant machine-learning model.