Keratoconus Classifier for Smartphone-based Corneal Topographer

Gairola, Siddhartha; Joshi, Pallavi; Balasubramaniam, Anand; Murali, Kaushik; Kwatra, Nipun; Jain, Mohit

doi:10.1109/embc48229.2022.9871744

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Furthermore, a public data set enables DL scientists worldwide to test new techniques to advance the accuracy of predictive analytics. Finally, it will be important to update the systematic review including newly published evidence [32][33][34][35][36][37] to capture progress in model development and to monitor reporting quality and guideline adherence.…”

Section: Implication For Research and Practicementioning

confidence: 99%

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

Bodmer,

Christensen,

Bachmann

et al. 2024

Cornea

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Purpose: The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for improving the accuracy of keratoconus detection. Deep learning (DL) offers considerable promise for improving the accuracy and speed of medical imaging interpretation. We establish an inventory of studies conducted with DL algorithms that have attempted to diagnose keratoconus. Methods: This systematic review was conducted according to the recommendations of the PRISMA statement. We searched (Pre-)MEDLINE, Embase, Science Citation Index, Conference Proceedings Citation Index, arXiv document server, and Google Scholar from inception to February 18, 2022. We included studies that evaluated the performance of DL algorithms in the diagnosis of keratoconus. The main outcome was diagnostic performance measured as sensitivity and specificity, and the methodological quality of the included studies was assessed using QUADAS-2. Results: Searches retrieved 4100 nonduplicate records, and we included 19 studies in the qualitative synthesis and 10 studies in the exploratory meta-analysis. The overall study quality was limited because of poor reporting of patient selection and the use of inadequate reference standards. We found a pooled sensitivity of 97.5% (95% confidence interval, 93.6%–99.0%) and a pooled specificity of 97.2% (95% confidence interval, 85.7%–99.5%) for topography images as input. Conclusions: Our systematic review found that the overall diagnostic performance of DL models to detect keratoconus was good, but the methodological quality of included studies was modest.

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Section: Implication For Research and Practicementioning

confidence: 99%

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

Bodmer,

Christensen,

Bachmann

et al. 2024

Cornea

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“…The diagnosis of keratoconus is performed by expensive and bulky medical devices called corneal topographers, which are not accessible to the masses, especially to people living in low-and middle-income countries. In a recent work [26,27], a low-cost smartphone-based corneal topographer, SmartKC, was proposed to diagnose keratoconus. However, it requires additional hardware like a 3D printed placido head, USB-powered LEDs, and a paper-based diffuser.…”

Section: Applicationsmentioning

confidence: 99%

“…Third, in the current setup we used a medical-grade retinoscope attached to a smartphone camera for data collection. In the future, we plan to use a cheap retinoscope, or a 3D-printed attachment imitating a retinoscope (similar to [26,27]), thus reducing the setup cost. Finally, for the current evaluation, the data was collected with a single smartphone by two data collectors.…”

Section: Limitationsmentioning

confidence: 99%

Towards Automating Retinoscopy for Refractive Error Diagnosis

Aggarwal

Gairola

Upadhyay

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Refractive error is the most common eye disorder and is the key cause behind correctable visual impairment, responsible for nearly 80% of the visual impairment in the US. Refractive error can be diagnosed using multiple methods, including subjective refraction, retinoscopy, and autorefractors. Although subjective refraction is the gold standard, it requires cooperation from the patient and hence is not suitable for infants, young children, and developmentally delayed adults. Retinoscopy is an objective refraction method that does not require any input from the patient. However, retinoscopy requires a lens kit and a trained examiner, which limits its use for mass screening. In this work, we automate retinoscopy by attaching a smartphone to a retinoscope and recording retinoscopic videos with the patient wearing a custom pair of paper frames. We develop a video processing pipeline that takes retinoscopic videos as input and estimates the net refractive error based on our proposed extension of the retinoscopy mathematical model. Our system alleviates the need for a lens kit and can be performed by an untrained examiner. In a clinical trial with 185 eyes, we achieved a sensitivity of 91.0% and specificity of 74.0% on refractive error diagnosis. Moreover, the mean absolute error of our approach was 0.75±0.67D on net refractive error estimation compared to subjective refraction measurements. Our results indicate that our approach has the potential to be used as a retinoscopy-based refractive error screening tool in real-world medical settings.

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“…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

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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

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Keratoconus Classifier for Smartphone-based Corneal Topographer

Cited by 4 publications

References 17 publications

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

Towards Automating Retinoscopy for Refractive Error Diagnosis

Examining the Challenges of Blood Pressure Estimation via Photoplethysmogram

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