Towards Automating Retinoscopy for Refractive Error Diagnosis

Aggarwal, Aditya; Gairola, Siddhartha; Upadhyay, Uddeshya; Vasishta, Akshay P.; Rao, Diwakar; Goyal, Aditya; Murali, Kaushik; Kwatra, Nipun; Jain, Mohit

doi:10.1145/3550283

“…Existing research work can be broadly divided into two categories: (1) approaches that are developed from first principles to imitate an established medical method for measurement or diagnosis 9,10 , and (2) approaches where input (sensor) data and corresponding gold-standard data are collected using a medical grade device and machine learning models are trained to discover a relationship between the input and output 11,12 . In this paper, we focus on the latter category.…”

Section: Introductionmentioning

confidence: 99%

Examining the Challenges of Blood Pressure Estimation via Photoplethysmogram

Mehta,

Jain,

Kwatra

et al. 2024

Preprint

0

View full text Add to dashboard Cite

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis

show abstract

“…Existing research work can be broadly divided into two categories: (1) approaches that are developed from first principles to imitate an established medical method for measurement or diagnosis 9,10 , and (2) approaches where input (sensor) data and corresponding gold-standard data are collected using a medical grade device and machine learning models are trained to discover a relationship between the input and output 11,12 . In this paper, we focus on the latter category.…”

mentioning

confidence: 99%

“Can’t Take the Pressure?”: Examining the Challenges of Blood Pressure Estimation via Pulse Wave Analysis

Mehta,

Kwatra,

Jain

et al. 2023

Preprint

Self Cite

1

0

View full text Add to dashboard Cite

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis.

show abstract