Comparison of machine learning–based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection

Prakash, Gaurav; Perera, Chandrashan; Jhanji, Vishal

doi:10.1007/s00417-023-06049-6

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…The majority of the included studies regarding keratoconus or corneal ectasia diagnosis with AI used data from corneal topography ( 47 , 51 – 53 , 56 , 57 , 67 , 73 , 74 , 77 , 78 , 83 , 91 , 92 , 94 , 100 ), Scheimpflug-based tomography ( 43 , 45 , 49 , 54 , 55 , 59 , 60 , 62 , 64 , 66 , 70 , 76 , 79 , 80 , 84 , 86 – 88 , 90 , 95 , 99 ), or optical coherence tomography (OCT) ( 31 , 46 , 50 , 53 , 68 , 69 , 72 , 80 , 84 ) as the input. For example, de Almeida et al.…”

Section: Resultsmentioning

confidence: 99%

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Goodman,

Zhu

2024

Front. Ophthalmol.

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IntroductionThe application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.MethodsWe conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.ResultsNinety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.DiscussionAlgorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

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Section: Resultsmentioning

confidence: 99%

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Goodman,

Zhu

2024

Front. Ophthalmol.

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Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach

Raza,

Akhtar,

Abualigah

et al. 2023

IEEE Access

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Driver behavior refers to the actions and attitudes of individuals behind the wheel of a vehicle. Poor driving behavior can have serious consequences, including accidents, injuries, and fatalities. One of the main disadvantages of poor driving behavior is the increased risk of road accidents, higher insurance premiums, fines, and even criminal charges. The primary aim of our study is to detect driver behavior early with high-performance scores. The publicly available smartphone motion sensor data is utilized to conduct our study experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method is proposed for feature engineering. The proposed LR-RFC method combines the logistic regression and random forest classifier for feature engineering from the motion sensor data. The original smartphone motion sensor data is input into the LR-RFC method, generating new probabilistic features. The newly extracted probabilistic features are then input to the applied machine learning methods for predicting driver behavior. The study results show that the proposed LR-RFC approach achieves the highest performance score. Extensive study experiments demonstrate that the random forest achieved the highest performance score of 99% using the proposed LR-RFC method. The performance is validated using k-fold cross-validation and hyperparameter optimization. Our novel proposed study has the potential to revolutionize the early detection of driver behavior to avoid road accidents.INDEX TERMS Machine learning, driver behavior, sensor data, feature engineering, ensemble learning.

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Comparison of machine learning–based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection

Cited by 2 publications

References 20 publications

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach

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