SmartKC

Gairola, Siddhartha; Bohra, Murtuza; Shaheer, Nadeem; Jayaprakash, Navya; Joshi, Pallavi; Balasubramaniam, Anand; Murali, Kaushik; Kwatra, Nipun; Jain, Mohit

doi:10.1145/3494982

Cited by 10 publications

(3 citation statements)

References 40 publications

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“…The contributors in [19] have published a paper in 2021 highlighting SmartKC: a low-cost, smartphone-based keratoconus diagnosis system comprising of a 3Dprinted Placido's disc attachment, an light-emitting diode (LED) light strip, and an intelligent smartphone app to capture the reflection of the Placido rings on the cornea. Research conducted in 2021 by [20] has led to presentation of a paper on diagnosability of keratoconus using deep learning with Placido disk-based corneal topography which shows the wide range of data being used for training the various models.…”

Section: Issn: 2088-8708 mentioning

confidence: 99%

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Priya

Shivanandappa²,

Devnath³

2023

IJECE

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<span lang="EN-US">The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time.</span>

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Section: Issn: 2088-8708 mentioning

confidence: 99%

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Priya

Shivanandappa²,

Devnath³

2023

IJECE

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“…For instance, a low-cost, point-of-care tool for evaluating corneal endothelial health has demonstrated comparable accuracy to "gold standard" specular microscopy, which costs tens of thousands of dollars 7 . Another smartphone-based device uses an illuminated 3D-printed attachment and an image analysis pipeline to diagnose keratoconus 8 . With regards to fluorescein examination, however, there does not yet exist a tool that is standalone, low-cost, quantitative, and noninvasive.…”

Section: Introductionmentioning

confidence: 99%

Pilot Clinical Validation of a Machine Learning Platform for Noninvasive Smartphone-Based Assessment of Corneal Epithelial Integrity

Zhang,

Pratap,

Young

et al. 2023

Preprint

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Purpose: Fluorescein staining (FS) is a standard method of assessing corneal epithelium (CE) integrity. However, the equipment and personnel required for FS may be unavailable in low-resource environments. We developed and validated a low-cost, noninvasive, and quantitative CE evaluation pipeline using a custom smartphone attachment and convolutional neural networks (CNNs). Methods: A 3D-printed smartphone attachment and placido disk illumination module was attached to a OnePlus 7 Pro smartphone. 26 smartphone-acquired images were obtained from 15 subjects, comprising a dataset including healthy eyes and corneal epitheliopathies of Oxford grade I-V. A classifier CNN was trained on 8 subjects (23,173 image patches) to identify areas of suspected epithelial disruption, and validated on 7 subjects (10,883 image patches). The fraction of disrupted corneal surface area (FDSA) was computed for each subject from the model output. Results were compared with FS slit lamp photos which were independently graded by two clinicians using the Oxford scheme. Results: FDSA showed promise as a non-invasive marker of CE integrity, with mean FDSA in the Oxford >II cohort being higher than the Oxford ≤II cohort (p = 0.04 and p = 0.09 using Oxford scores from each clinician, respectively). Additionally, areas of CE disruption identified by our smartphone-based technique showed qualitative concordance with those revealed by FS. Conclusions: Our technique for smartphone-based CE imaging and automated analysis is a promising low-cost, noninvasive method to quantitatively evaluate the CE. Translational Relevance: This tool can be used to evaluate ocular surface disease in low-resource regions.

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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.…”

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

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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.

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SmartKC

Cited by 10 publications

References 40 publications

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Pilot Clinical Validation of a Machine Learning Platform for Noninvasive Smartphone-Based Assessment of Corneal Epithelial Integrity

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

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