Deep Learning Renal Denervation Efficacy Prediction
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Solution Overview
Problem
Current methods lack an effective way to predict the efficacy of renal denervation therapy in reducing hypertension, as they do not adequately assess the contribution of renal sympathetic nerves to hypertension in individual patients.
Innovation Solution
A system utilizing deep learning models to predict renal denervation efficacy by analyzing pulse information from the wrists of patients, combined with other patient metrics, to generate a score indicative of the therapy's potential to reduce hypertension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If renal denervation therapy is performed without predictive assessment, then treatment can be provided to patients, but the efficacy in reducing hypertension cannot be reliably predicted
Solution Approach 1:
The patent applies preliminary action by performing pulse waveform analysis and deep learning model prediction before renal denervation therapy is administered. The system collects pulse information from patients' wrists, processes it through a deep learning model trained on patient metrics and therapy outcomes, and generates a predictive score indicating the likelihood of hypertension reduction. This preliminary assessment allows clinicians to identify suitable candidates before undergoing the actual therapy, thereby improving reliability of efficacy prediction without requiring complex invasive pre-assessment procedures.
2Measurement precision
If traditional patient assessment methods are used, then the assessment process is simple, but the contribution of renal sympathetic nerves to hypertension cannot be adequately evaluated
Solution Approach 1:
The patent uses pulse waveform information as an intermediary to indirectly assess the contribution of renal sympathetic nerves to hypertension. Instead of directly measuring renal sympathetic nerve activity, which would be complex and invasive, the system captures non-invasive pulse signals from the patient's wrist, extracts relevant features, and uses a deep learning model to infer renal sympathetic contribution. This intermediary approach enables precise measurement of renal sympathetic influence on hypertension while avoiding the technical difficulties of direct measurement.
3Productivity
If renal denervation therapy is provided without patient selection, then all patients can receive treatment, but resources may be wasted on patients unlikely to benefit
Solution Approach 1:
The patent applies parameter changes by transforming raw pulse waveform data into meaningful predictive parameters through signal processing and deep learning analysis. The system extracts features from pulse waveforms (such as amplitude, frequency, and morphological characteristics) and combines them with patient metrics to generate a predictive score parameter. This parameter indicates the probability of achieving target hypertension reduction from renal denervation therapy, enabling efficient patient selection and preventing resource waste on unlikely candidates while maintaining individualized prediction capability.
Data Source
AI summary
Example devices, systems, and techniques predict renal denervation efficacy for reducing hypertension in a patient based on pulse information. For example, a system may include processing circuitry configured to obtain pulse information representative of pulses from both wrists of a patient, obtain a plurality of values representative of respective patient metrics for the patient, and apply the pulse information and the plurality of values to a deep learning model trained to represent a relationship of the pulse information and the patient metrics to an efficacy of renal denervation in reducing hypertension. In some examples, responsive to applying the pulse information and the plurality of values to the deep learning model, the processing circuitry obtains, from the deep learning model, a score indicative of renal denervation efficacy in reducing hypertension for the patient, and generates a graphical user interface comprising a graphical representation of the score for the patient.