Accurate prediction of myopic progression and high myopia by machine learning
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PROVIDER: S-EPMC10981449 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
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Project description:ImportanceIndividuals with high myopia younger than 18 years are at relatively high risk of progressively worsening myopic maculopathy. Additional studies are needed to investigate the progression of myopic maculopathy in this age group, as well as the risk factors associated with progression.ObjectiveTo investigate the 4-year progression of myopic maculopathy in children and adolescents with high myopia, and to explore potential risk factors.Design, setting, and participantsThis hospital-based observational study with 4-year follow-up included a total of 548 high myopic eyes (spherical power -6.00 or less diopters) of 274 participants aged 7 to 17 years. Participants underwent comprehensive ophthalmic examination at baseline and 4-year follow-up. Myopic maculopathy was accessed by the International Photographic Classification and Grading System. The data analysis was performed from August 1 to 15, 2023.Main outcomes and measuresThe progression of myopic maculopathy progression over 4 years and associated risk factors.ResultsThe 4-year progression of myopic maculopathy was found in 67 of 548 eyes (12.2%) of 274 participants (138 girls [50.4%] at baseline and 4-year follow-up) with 88 lesion changes, including new signs of the tessellated fundus in 16 eyes (18.2%), diffuse atrophy in 12 eyes (13.6%), patchy atrophy in 2 eyes (2.3%), lacquer cracks in 9 eyes (10.2%), and enlargement of diffuse atrophy in 49 eyes (55.7%). By multivariable analysis, worse best-corrected visual acuity (odds ratio [OR], 6.68; 95% CI, 1.15-38.99; P = .04), longer axial length (AL) (OR, 1.73; 95% CI, 1.34-2.24; P < .001), faster AL elongation (OR, 302.83; 95% CI, 28.61-3205.64; P < .001), and more severe myopic maculopathy (diffuse atrophy; OR, 4.52; 95% CI, 1.98-10.30; P < .001 and patchy atrophy; OR, 3.82; 95% CI, 1.66-8.80; P = .002) were associated with myopic maculopathy progression.Conclusions and relevanceIn this observational study, the progression of myopic maculopathy was observed in approximately 12% of pediatric high myopes for 4 years. The major type of progression was the enlargement of diffuse atrophy. Risk factors for myopic maculopathy progression were worse best-corrected visual acuity, longer AL, faster AL elongation, and more severe myopic maculopathy. These findings support consideration of follow-up in these individuals and trying to identify those at higher risk for progression.
| S-EPMC10811590 | biostudies-literature
Project description:Accurate detection and timely care for patients with high myopia present significant challenges. We developed a deep learning (DL) system enhanced by a self-supervised learning (SSL) approach to improve the automatic diagnosis of myopic maculopathy (MM). Using a dataset of 7,906 images from the Shanghai High Myopia Screening Project and a public validation set of 1,391 images from MMAC2023, our method significantly outperformed conventional techniques. Internally, it achieved 96.8% accuracy, 83.1% sensitivity, and 95.6% specificity, with AUC values of 0.982 and 0.999. Externally, it maintained 89.0% accuracy, 71.7% sensitivity, and 87.8% specificity, with AUC values of 0.978 and 0.973. The model's Cohen's kappa values exceeded 0.8, indicating substantial agreement with retinal experts. Our SSL-enhanced DL approach offers high accuracy and potential to enhance large-scale myopia screenings, demonstrating broader significance in improving early detection and treatment of MM.
| S-EPMC11359982 | biostudies-literature
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:BackgroundIn recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. ML models appear to demonstrate promising accuracy in the diagnosis of complex diseases, as well as in predicting disease progression and prognosis. Some studies have applied it to ophthalmology, primarily for the diagnosis of pathologic myopia and high myopia-associated glaucoma, as well as for predicting the progression of high myopia. ML-based detection still requires evidence-based validation to prove its accuracy and feasibility.ObjectiveThis study aims to discern the performance of ML methods in detecting high myopia and pathologic myopia in clinical practice, thereby providing evidence-based support for the future development and refinement of intelligent diagnostic or predictive tools.MethodsPubMed, Cochrane, Embase, and Web of Science were thoroughly retrieved up to September 3, 2023. The prediction model risk of bias assessment tool was leveraged to appraise the risk of bias in the eligible studies. The meta-analysis was implemented using a bivariate mixed-effects model. In the validation set, subgroup analyses were conducted based on the ML target events (diagnosis and prediction of high myopia and diagnosis of pathological myopia and high myopia-associated glaucoma) and modeling methods.ResultsThis study ultimately included 45 studies, of which 32 were used for quantitative meta-analysis. The meta-analysis results unveiled that for the diagnosis of pathologic myopia, the summary receiver operating characteristic (SROC), sensitivity, and specificity of ML were 0.97 (95% CI 0.95-0.98), 0.91 (95% CI 0.89-0.92), and 0.95 (95% CI 0.94-0.97), respectively. Specifically, deep learning (DL) showed an SROC of 0.97 (95% CI 0.95-0.98), sensitivity of 0.92 (95% CI 0.90-0.93), and specificity of 0.96 (95% CI 0.95-0.97), while conventional ML (non-DL) showed an SROC of 0.86 (95% CI 0.75-0.92), sensitivity of 0.77 (95% CI 0.69-0.84), and specificity of 0.85 (95% CI 0.75-0.92). For the diagnosis and prediction of high myopia, the SROC, sensitivity, and specificity of ML were 0.98 (95% CI 0.96-0.99), 0.94 (95% CI 0.90-0.96), and 0.94 (95% CI 0.88-0.97), respectively. For the diagnosis of high myopia-associated glaucoma, the SROC, sensitivity, and specificity of ML were 0.96 (95% CI 0.94-0.97), 0.92 (95% CI 0.85-0.96), and 0.88 (95% CI 0.67-0.96), respectively.ConclusionsML demonstrated highly promising accuracy in diagnosing high myopia and pathologic myopia. Moreover, based on the limited evidence available, we also found that ML appeared to have favorable accuracy in predicting the risk of developing high myopia in the future. DL can be used as a potential method for intelligent image processing and intelligent recognition, and intelligent examination tools can be developed in subsequent research to provide help for areas where medical resources are scarce.Trial registrationPROSPERO CRD42023470820; https://tinyurl.com/2xexp738.
| S-EPMC11748443 | biostudies-literature
Project description:Qualitative analysis of fundus photographs enables straightforward pattern recognition of advanced pathologic myopia. However, it has limitations in defining the classification of the degree or extent of early disease, such that it may be biased by subjective interpretation. In this study, we used the fovea, optic disc, and deepest point of the eye (DPE) as the three major markers (i.e., key indicators) of the posterior globe to quantify the relative tomographic elevation of the posterior sclera (TEPS). Using this quantitative index from eyes of 860 myopic patients, support vector machine based machine learning classifier predicted pathologic myopia an AUROC of 0.828, with 77.5% sensitivity and 88.07% specificity. Axial length and choroidal thickness, the existing quantitative indicator of pathologic myopia only reached an AUROC of 0.758, with 75.0% sensitivity and 76.61% specificity. When all six indices were applied (four TEPS, AxL, and SCT), the discriminative ability of the SVM model was excellent, demonstrating an AUROC of 0.868, with 80.0% sensitivity and 93.58% specificity. Our model provides an accurate modality for identification of patients with pathologic myopia and may help prioritize these patients for further treatment.
| S-EPMC7997908 | biostudies-literature
Project description:The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both in vitro stability and in vivo biological function of diverse classes of proteins. During protein therapeutics development, deamidation liabilities that are overlooked necessitate expensive and time-consuming remediation strategies, sometimes leading to termination of the project. In this paper, we apply machine learning to a large (n = 776) liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset of monoclonal antibody peptides to create computational models for the post-translational modification asparagine deamidation, using the random decision forest method. We show that our categorical model predicts antibody deamidation with nearly 5% increased accuracy and 0.2 MCC over the best currently available models. Surprisingly, our model also paces or outperforms advanced and conventional models on an independent non-antibody dataset. In addition to deamidation probability, we are able to accurately predict deamidation rate (R2 = 0.963 and Q2 = 0.822), a capability with no peer in current models. This method should enable significant improvement in protein candidate selection, especially in biopharmaceutical development, and can be applied with similar accuracy to enzymes, monoclonal antibodies, next-generation formats, vaccine component antigens, and gene therapy vectors such as adeno-associated virus.
| S-EPMC6923510 | biostudies-literature
Project description:For practical applications, molecules often exist in an aggregate state. Therefore, it is of great value if one can predict the performance of molecules when forming aggregates, for example, aggregation-induced emission (AIE) or aggregation-caused quenching (ACQ). Herein, a database containing AIE/ACQ molecules reported in the literature is first established. Through training, these machine learning (ML) models can build up the structure-property relationship and thus implement fast prediction of AIE/ACQ properties. To this end, a multi-modal approach is proposed, multiple prediction methods are compared and designed, and thus an ensemble strategy is developed. First, multiple molecular descriptors are considered at the same time, major features are extracted by dimensionality reduction, and multi-modal features are synthesized. Then, several state-of-the-art methods are designed and compared to analyze the advantages of the different methods. Finally, the ensemble strategy combines the advantages of the multiple methods to obtain the final prediction result. The reliability of this approach in an unknown molecular space is further verified by three newly designed molecules. Reasonable consistency between model predictions and experimental outcomes is obtained. The result indicates that ML can be a powerful tool to predict molecular properties in the aggregated state, thus accelerating the development of solid-state optical materials.
| S-EPMC8760175 | biostudies-literature
Project description:Allostery is a fundamental process in regulating protein activities. The discovery, design, and development of allosteric drugs demand better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we define a baseline model for allosteric site prediction and present a computational model using automated machine learning. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 82.7% of allosteric pockets appearing among the top three positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (PASSer) to facilitate allosteric drug discovery.
| S-EPMC9309527 | biostudies-literature
Project description:Dataset supporting the identification of machine learning-lead validation of SET8 substrates using targeted mass spectrometry (PRM-MS).
2025-02-22 | PXD050037 | JPOST Repository
Project description:We enrolled 500 highly myopic eyes and 50 controls in this hospital-based prospective cohort study. The fixation ellipse angle and area in terms of the bivariate contour ellipse area (BCEA) were measured with Macular Integrity Assessment microperimetry. Optic disc tilt and rotation were evaluated with retinal images. The associations between fixation and optic disc changes were assessed. Both 63% and 95% BCEA correlated positively with axial length (AL) (both r = 0.230, P = 0.001) in highly myopic group, and were significantly higher than the control group (both P < 0.001). The direction of fixation ellipse presented clockwise rotation in the right eyes and anti-clockwise rotation in the left eyes with the increase of AL in highly myopic group (AL ≥30 vs <30 mm: OD 76.12 ± 51.17°: vs 90.60° ± 51.28°, P = 0.029; OS 94.73 ± 57.45° vs 87.82 ± 55.15°, P = 0.371). The angle between the long axis of the fixation ellipse and the long axis of the optic disc (AngleF-D) distributed in various directions: 0-30° (34.6% almost parallel) ≈60-90° (34.4% almost vertical) >30-60° (31% oblique). AngleF-D increased slightly with the AL (r = 0.105, P = 0.024). In highly myopic eyes, fixation stability decreased with the AL, and superior rotation of the fixation ellipse increased with AL. The long axis of fixation ellipse and the long axis of optic disc became less parallel to each other with increasing AL. Our data may provide clues for improvement of fixation evaluation designs of biometric instruments.
| S-EPMC6482140 | biostudies-literature