Mathematical modeling and glaucoma: the need for an individualized approach to risk assessment

Vercellin, Alice Chandra Verticchio; Harris, Alon; Cordell, Jareau Vance; Do, Thai; Moroney, James; Belamkar, Aditya; Siesky, Brent

doi:10.35119/maio.v1i1.18

Cited by 3 publications

(2 citation statements)

References 75 publications

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“…Most studies on glaucoma are based on the use of mechanistic models [29][30][31][32][33]. Nowadays, there are great hopes for the use of a wide range of methods of machine learning (ML) -arti cial intelligence (AI) as a tool for diagnosis, choice of treatments and prediction of outcomes for PACD.…”

Section: Application Of Arti Cial Intelligence In Ophthalmology: Mach...mentioning

confidence: 99%

Machine learning couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Kurysheva

Rodionova²,

Pomerantsev³

et al. 2023

Preprint

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Background: Primary angle-closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health-risk assessment based on multi-level diagnostics and machine learning couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression. Aim: To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE). Methods: The multi-parametric data analysis was used to develop models predicting individual outcome of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE)+30 (LPI) patients with PAC. Results and data interpretation in the framework of 3P medicine: Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. The multi-level diagnostics is the key-tool in the overall advanced approach. To this end, for the future application of AI in the area it is strongly recommended to consider 1. clinically relevant phenotyping applicable to advanced population screening 2. systemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transition 3. clinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis.

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Section: Application Of Arti Cial Intelligence In Ophthalmology: Mach...mentioning

confidence: 99%

Machine learning couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Kurysheva

Rodionova²,

Pomerantsev³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Most studies on glaucoma are based on the use of mechanistic models [ 29 – 33 ]. Nowadays, there are great hopes for the use of a wide range of methods of machine learning (ML)–artificial intelligence (AI) as a tool for diagnosis, choice of treatments, and prediction of outcomes for PACD.…”

Section: Introductionmentioning

confidence: 99%

Machine learning–couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Kurysheva,

Rodionova,

Pomerantsev

et al. 2023

EPMA Journal

View full text Add to dashboard Cite

Background Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health risk assessment based on multi-level diagnostics and machine learning–couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression. Aim To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE). Methods The multi-parametric data analysis was used to develop models predicting individual outcomes of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty-seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE) + 30 (LPI) patients with PAC. Results and data interpretation in the framework of 3P medicine Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following: Clinically relevant phenotyping applicable to advanced population screening Systemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transition Clinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis.

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Uncertainty propagation and sensitivity analysis: results from the Ocular Mathematical Virtual Simulator

Prud'Homme

Sala

Szopos

2021

MBE

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<abstract><p>We propose an uncertainty propagation study and a sensitivity analysis with the Ocular Mathematical Virtual Simulator, a computational and mathematical model that predicts the hemodynamics and biomechanics within the human eye. In this contribution, we focus on the effect of intraocular pressure, retrolaminar tissue pressure and systemic blood pressure on the ocular posterior tissue vasculature. The combination of a physically-based model with experiments-based stochastic input allows us to gain a better understanding of the physiological system, accounting both for the driving mechanisms and the data variability.</p></abstract>

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Mathematical modeling and glaucoma: the need for an individualized approach to risk assessment

Cited by 3 publications

References 75 publications

Machine learning couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Machine learning couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Machine learning–couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Uncertainty propagation and sensitivity analysis: results from the Ocular Mathematical Virtual Simulator

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