Romanian journal of ophthalmology · 2024
A neural network model for predicting the effectiveness of treatment in patients with neovascular glaucoma associated with diabetes mellitus.
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Guzun Olga Volodymyrivna, Zadorozhnyy Oleg Serhiyovich, Vychuzhanin Volodymyr Viktorovych, Khramenko Natalia Ivanivna, Velichko Liudmyla Mykolayivna, Korol Andrii Rostyslavovich, Cușnir Valeriu Nicon, Dumbrăveanu Lilia Gheorghe, Cușnir Vitalie Valeriu
Аннотация
INTRODUCTION: The study hypothesizes that neural networks can be an effective tool for predicting treatment outcomes in patients with diabetic neovascular glaucoma (NVG), considering not only baseline intraocular pressure (IOP) values but also inflammation and intraocular microcirculation indicators.
OBJECTIVE: To investigate the diagnostic significance of inflammation and intraocular blood circulation indicators in a neural network model predicting the effectiveness of transscleral cyclophotocoagulation (TSC CPC) treatment in patients with NVG of diabetic origin.
METHODS: This retrospective cohort study included 127 patients (127 eyes; aged Me 65.0 years) with painful diabetic NVG and 20 healthy individuals (aged Me 61.5 years) as an immunological control. All patients underwent TSC CPC with a diode laser. Treatment success was defined as achieving an IOP level of ≤ 21 mmHg and maintaining or improving best-corrected visual acuity (BCVA) after 12 months of observation. Preoperative systemic immune-inflammation index (SII = platelets × [neutrophils/lymphocytes]) and systemic inflammation response index (SIRI = neutrophils × [monocytes/lymphocytes]) were calculated. We assessed the values of volumetric pulse blood filling, determined by the rheographic coefficient (RQ, 0/00), using the rheoophthalmography (ROG) method. Multiple regression analysis was used to conclude the significance of treatment efficacy based on initial clinical and laboratory indicators, followed by constructing a prediction model in the neural network.
RESULTS: The development of the neural network model identified the most significant "input" parameters: SIRI (100%), RQ (85.7%), and SII (80.7%), which significantly influenced treatment success. The sensitivity of the neural network model was 100%, specificity was 30%, and the percentage of correctly predicted events during testing on the control group was 92.9%.
CONCLUSIONS: Neural network-based prediction of transscleral cyclophotocoagulation effectiveness for patients with diabetic neovascular glaucoma allows for a sufficiently accurate forecast of treatment success with a probability of 92.9%. We believe the in-time correction of systemic inflammation and intraocular blood circulation can significantly reduce intraocular pressure, preserve visual acuity, and improve the quality of life in patients with diabetic NVG after TSC CPC. Further research is required to support these findings.
OBJECTIVE: To investigate the diagnostic significance of inflammation and intraocular blood circulation indicators in a neural network model predicting the effectiveness of transscleral cyclophotocoagulation (TSC CPC) treatment in patients with NVG of diabetic origin.
METHODS: This retrospective cohort study included 127 patients (127 eyes; aged Me 65.0 years) with painful diabetic NVG and 20 healthy individuals (aged Me 61.5 years) as an immunological control. All patients underwent TSC CPC with a diode laser. Treatment success was defined as achieving an IOP level of ≤ 21 mmHg and maintaining or improving best-corrected visual acuity (BCVA) after 12 months of observation. Preoperative systemic immune-inflammation index (SII = platelets × [neutrophils/lymphocytes]) and systemic inflammation response index (SIRI = neutrophils × [monocytes/lymphocytes]) were calculated. We assessed the values of volumetric pulse blood filling, determined by the rheographic coefficient (RQ, 0/00), using the rheoophthalmography (ROG) method. Multiple regression analysis was used to conclude the significance of treatment efficacy based on initial clinical and laboratory indicators, followed by constructing a prediction model in the neural network.
RESULTS: The development of the neural network model identified the most significant "input" parameters: SIRI (100%), RQ (85.7%), and SII (80.7%), which significantly influenced treatment success. The sensitivity of the neural network model was 100%, specificity was 30%, and the percentage of correctly predicted events during testing on the control group was 92.9%.
CONCLUSIONS: Neural network-based prediction of transscleral cyclophotocoagulation effectiveness for patients with diabetic neovascular glaucoma allows for a sufficiently accurate forecast of treatment success with a probability of 92.9%. We believe the in-time correction of systemic inflammation and intraocular blood circulation can significantly reduce intraocular pressure, preserve visual acuity, and improve the quality of life in patients with diabetic NVG after TSC CPC. Further research is required to support these findings.