Prediction of Pentacam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression model

Fırat, Murat; Çınar, Ahmet; Çankaya, Cem; Fırat, İlknur Tuncer; Tuncer, Taner

doi:10.1016/j.compbiomed.2022.105541

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Six studies featured the use of AI in characterizing the response to different treatment modalities, such as by predicting postoperative outcomes or the need for future intervention ( Supplementary Table 5 ) ( 136 – 141 ). Both Valdés-Mas 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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“…Knowledge sources use the data store (blackboard) to solve problems by writing them down and solving them on the blackboard. All knowledge resources add to or change the answers provided by previous knowledge sources [10]. As a result, all knowledge sources collaborate to solve any problem.…”

Section: Blackboard Architecturementioning

confidence: 99%

“…The following factors may have an impact on the system architecture style; i) system acquisition, ii) technical surroundings, iii) organizational structure, and iv) knowledge of masonry. There are a few other important factors to consider when mapping non-functional requirements into system architecture styles: i) the system architecture style should be created in such a way that it meets all the important quality requirements of the customer [9], ii) the system architect should not go into implementation details [10], iii) to maximize production, system architecture should make use of as many system resources as possible [11], and iv) the system architecture should be adaptable to future changes [12]. The design of the system architecture describes how to set up the system, what the system's behavior will be, how risk management is carried out, and how to display the system in terms of components so that programmers can reuse the same components and improve system quality.…”

Section: Introductionmentioning

confidence: 99%

A systematic review of non-functional requirements mapping into architectural styles

et al. 2023

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Fortunately, the software attracted enough businesses to the market, allowing them to earn money in less time with less work and more accurate results. Software development life cycle (SDLC) is used for software development as it is responsible for system functionality, efficiency, maintainability, and any other non-functional system requirements. Each stage of the SDLC process is critical. However, software requirements and software architecture are both fundamental activities that play a vital role in all other SDLC stages. Non-functional requirements are critical to the success of any software because they explain all system quality attributes such as complexity, reliability, security, and maintainability, among others. The architectural styles assist you in determining which architecture may be best for your project requirements. This paper discusses several of the most important architectural styles that are best suited for mapping desired non-functional requirements for software development, as well as their comparison based on various quality attributes (non-functional requirements).

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“…This information is a consequence of cooperation and relationship so that exposed a construction called interpersonal organization. The greater part of designs which can be called interpersonal organization was made around us [2].…”

Section: Introductionmentioning

confidence: 99%

The similarity between Disease and Drug Network in Link Prediction

Azam

Nouman

2022

EAI Endorsed Trans Mob Com Appl

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Nowadays, data records are being transferred entirely to digital platforms, and data have become embodied and measurable. In this study, to observe the relationship between disease and drug, we first constructed disease and drug networks. These networks consist of a disease diagnosis and drugs written by doctors. After the disease and drug networks were generated, a link prediction was done concerning similarity values between nodes. Experimental results show that the proposed method finds satisfactory results. By examining these constructions and connections it is feasible to achieve affinities, similitudes, and patterns and it is likewise conceivable to make it feasible to achieve.

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Prediction of Pentacam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression model

Cited by 8 publications

References 14 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

A systematic review of non-functional requirements mapping into architectural styles

The similarity between Disease and Drug Network in Link Prediction

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