scholarly journals Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

2021 ◽  
Vol 15 ◽  
Author(s):  
Mona Matar ◽  
Suleyman A. Gokoglu ◽  
Matthew T. Prelich ◽  
Christopher A. Gallo ◽  
Asad K. Iqbal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Radiation Exposure ◽  
Space Radiation ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Support Vector ◽  
Control Group ◽  
Significant Finding ◽  
Dose Dependent

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in attentional set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing machine learning (ML) classifiers such as the Gaussian naïve Bayes, support vector machine, and artificial neural networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreen performance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4He in SD and 28Si in CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for more robust predictions of cognitive decrements due to space radiation exposure.

scholarly journals Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 233
Author(s):  
Jonathan Z.L. Zhao ◽  
Eliseos J. Mucaki ◽  
Peter K. Rogan
Keyword(s):  
Machine Learning ◽  
Ionizing Radiation ◽  
Radiation Exposure ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Error Rates ◽  
Support Vector ◽  
Dose Estimation ◽  
Gene Signatures ◽  
Ionizing Radiation Exposure

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% (DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

scholarly journals Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 233 ◽  
Author(s):  
Jonathan Z.L. Zhao ◽  
Eliseos J. Mucaki ◽  
Peter K. Rogan
Keyword(s):  
Machine Learning ◽  
Ionizing Radiation ◽  
Radiation Exposure ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Error Rates ◽  
Support Vector ◽  
Dose Estimation ◽  
Gene Signatures ◽  
Ionizing Radiation Exposure

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% (DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

scholarly journals Employment of Machine Learning Models Yields Highly Accurate Hematological Disease Prediction from Raw Flow Cytometry Matrix Data without the Need for Visualization or Human Intervention

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 11-11
Author(s):  
Martha-Lena Müller ◽  
Niroshan Nadarajah ◽  
Kapil Jhalani ◽  
Inseok Heo ◽  
William Wetton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Automated Analysis ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Tree Model ◽  
The Third ◽  
Lymphatic Leukemia ◽  
Boosted Decision Trees ◽  
Human Operators

Background: Machine Learning (ML) offers automated data processing substituting various analysis steps. So far it has been applied to flow cytometry (FC) data only after visualization which may compromise data by reduction of data dimensionality. Automated analysis of FC raw matrix data has not yet been pursued. Aim: To establish as proof of concept an ML-based classifier processing FC matrix data to predict the correct lymphoma type without the need for visualization or human analysis and interpretation. Methods: A set of 6,393 uniformly analyzed samples (Navios cytometers, Kaluza software, Beckman Coulter, Miami, FL) was used for training (n=5,115) and testing (n=1,278) of different ML models. Entities were chronic lymphatic leukemia (CLL) 1103 (training) and 279 (testing), monoclonal B-cell lymphocytosis (MBL, 831/203), CLL with increased prolymphocytes (CLL-PL, 649/161), lymphoplasmacytic lymphoma (LPL, 560/159), hairy cell leukemia (HCL, 328/88), mantle cell lymphoma (MCL, 259/53), marginal zone lymphoma (MZL, 90/28), follicular lymphoma (FL, 84/16), no lymphoma (1211/291). Three tubes comprising 11 parameters per tube were applied. Besides scatter signals analyzed antigens included: CD3, CD4, CD5, CD8, CD10, CD11c, CD19, CD20, CD22, CD23, CD25, CD38, CD45, CD56, CD79b, CD103, FMC7, HLA-DR, IgM, Kappa, Lambda. Measurements generated LMD files with 50,000 rows of data for each of the 11 parameters. After removing the saturated values (≥ 1023) we produced binned histograms with 16 predefined frequency bins per parameter. Histograms were converted to cumulative distribution functions (CDF) for respective parameters and concatenated to produce a 16x11 matrix per each tube. Following the assumption of independence of parameters this simplification of concatenating CDFs represents the same information as if they were jointly distributed. The first matrix-based classifier was a decision tree model (DT), the second a deep learning model (DL) and the third was an XGBoost (XG) model, an implementation of gradient boosted decision trees ideal for structured tabular data (such as LMD files). The first set of analyses included only three classes which are readily separated by human operators: 1) CLL, 2) HCL, 3) no lymphoma. The second set included all nine entities but grouped into four classes: 1) CD5+ lymphoma (CLL, MBL, CLL-PL, MCL), 2) HCL, 3) other CD5- lymphoma (LPL, MZL, FL), 4) no lymphoma. The third set included each of the nine entities as its own class. Results: Analyzing the three classes from the first set (CLL, HCL, no lymphoma) the models achieved accuracies of 94% (DT), 95% (DL) and 96% (XG) when including all cases. By analysis of cases with prediction probabilities above 90%, DT now reached 97%, DL 97% and XG 98% accuracy, whilst losing 38%, 8% and 6% of samples, respectively. We further observed that accuracy was also dependent on the size of the pathologic clone, which is in line with the experiences from human experts with very small clones (≤ 0.1% of leukocytes) representing a major challenge regarding their correct classification. Focusing on cases with clones > 0.1% but considering all prediction probabilities accuracies were 96% (DT), 97% (DL) and 98% (XG), with loss of 5% of samples for each model. Considering cases only with prediction probabilities > 90% and clones > 0.1% accuracies were 97% (DT), 99% (DL) and 99% (XG) whilst losing 38%, 9% and 9% of samples, respectively. Further analyses were performed applying the best model based on results above, i.e. XG. Analyzing four classes in the second set of analyses (CD5+ lymphoma, HCL, other CD5- lymphoma, no lymphoma) and considering cases only with prediction probabilities > 95% and clones > 0.1% accuracy was 96% while losing 28% of samples. In the third set of analyses with each entity assigned its own class and again considering cases only with prediction probabilities > 95% and clones > 0.1% accuracy was 93% while losing 28% of samples. Conclusions: This first ML-based classifier using the XGboost model with transforming FC matrix data to concatenated distributions, is capable of correctly assigning the vast majority of lymphoma samples analyzing FC raw data without visualization or human interpretation. Cases that need further attention by human experts will be flagged but will not account for more than 30% of all cases. This data will be extended in a prospective blinded study (clinicaltrials.gov NCT4466059). Disclosures Heo: AWS: Current Employment. Wetton:AWS: Current Employment.

scholarly journals Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Support Vector ◽  
Alzheimer’S Disease Dementia

Abstract Background An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia. Methods We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. Results Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

scholarly journals Machine Learning Models on ADC Features to Assess Brain Changes of Children With Pierre Robin Sequence

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Wang ◽  
Feng Yang ◽  
Meijiao Zhu ◽  
Ming Yang
Keyword(s):  
Machine Learning ◽  
Validation Dataset ◽  
Support Vector ◽  
Control Group ◽  
Pierre Robin Sequence ◽  
Learning Models ◽  
Robin Sequence ◽  
Brain Changes ◽  
Pierre Robin ◽  
Machine Learning Models

In order to evaluate brain changes in young children with Pierre Robin sequence (PRs) using machine learning based on apparent diffusion coefficient (ADC) features, we retrospectively enrolled a total of 60 cases (42 in the training dataset and 18 in the testing dataset) which included 30 PRs and 30 controls from the Children's Hospital Affiliated to the Nanjing Medical University from January 2017–December 2019. There were 21 and nine PRs cases in each dataset, with the remainder belonging to the control group in the same age range. A total of 105 ADC features were extracted from magnetic resonance imaging (MRI) data. Features were pruned using least absolute shrinkage and selection operator (LASSO) regression and seven ADC features were developed as the optimal signatures for training machine learning models. Support vector machine (SVM) achieved an area under the receiver operating characteristic curve (AUC) of 0.99 for the training set and 0.85 for the testing set. The AUC of the multivariable logistic regression (MLR) and the AdaBoost for the training and validation dataset were 0.98/0.84 and 0.94/0.69, respectively. Based on the ADC features, the two groups of cases (i.e., the PRs group and the control group) could be well-distinguished by the machine learning models, indicating that there is a significant difference in brain development between children with PRs and normal controls.

scholarly journals Machine Learning Methods for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease. A Systematic Review.

2021 ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Multidimensional Data ◽  
Support Vector

Abstract Background: Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease.Methods: We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from Mild Cognitive Impairment to Alzheimer’s disease. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Results: Most studies used Magnetic Resonance Image and Positron Emission Tomography data but also Magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease Neuroimage Initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, biological, and behavioral) achieved the best performance. Conclusions: Although performance of the different models still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

scholarly journals Daily Smartphone Overdependence Screening Model using Support Vector Machine (Preprint)

2018 ◽  
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
In Hye Yook ◽  
Mun Joo Choi ◽  
Dai-Jin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Smartphone Application ◽  
Health Clinic ◽  
Psychiatric Disease ◽  
Support Vector ◽  
Control Group ◽  
Smartphone Addiction ◽  
Screening Model ◽  
Svm Model

BACKGROUND Smartphone overdependence has caused many social problems. To overcome these problems, it is necessary to screen and identify smartphone overdependence before it becomes a serious issue. OBJECTIVE We aimed to developed a daily smartphone overdependence screening model using a Support Vector Machine (SVM). METHODS We used smartphone application usage time and frequency data from 224 participants whose ages ranged from their 20s to their 40s. We classified the participants into two groups the smartphone usage control group (SUC) and the smartphone usage disorder addiction group (SUD) using the Korean Smartphone Addiction Proneness Scale (K-SAPS) for Adults. We built a 3-dimensional tensor as the input of machine learning training. This study used the SVM to develop a daily smartphone overdependence screening model. We compared the model performance between the SVM, the Artificial Neural Network(ANN) and the Logistic Regression. RESULTS We identified the frequency of smartphone application usage, age, and marital status as the dominant features of screening smartphone overdependence. Using these features as the inputs of the SVM machine learning model showed a 90% of accuracy for the smartphone overdependence screening. CONCLUSIONS We developed a SVM model, which is a tool for self-control of smartphone daily usage. As a pre-testing tool before visiting a mental health clinic. The SVM model is a powerful analysis method for smartphone overdependence screening. Notably, psychiatry studies have used the SVM when identifying a psychiatric disease. We suggest using the SVM model for smartphone overdependence screening as a smartphone application or intervention system for smartphone dependency management.

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer’s Disease Tests: A Multivariate and Machine Learning Study

2018 ◽  
Vol 45 (1-2) ◽  
pp. 38-48 ◽  
Author(s):  
Chavit Tunvirachaisakul ◽  
Thitiporn Supasitthumrong ◽  
Sookjareon Tangwongchai ◽  
Solaphat Hemrunroj ◽  
Phenphichcha Chuchuen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Support Vector ◽  
Test Machine ◽  
Characteristic Analysis ◽  
Adaptive Boosting ◽  
Normal Controls

Background: The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer’s dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. Objectives: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. Methods: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). Results: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. Conclusions: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.

scholarly journals Machine Learning methods for predicting conversion from mild cognitive impairment to Alzheimer’s disease. A systematic review.

2021 ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Multidimensional Data ◽  
Support Vector

Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease (AD) being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment (MCI), sometimes a prodromal stage of AD. We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from MCI to AD. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Most studies used MRI and PET data but also MEG. The datasets were mainly extracted from the ADNI database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimage, clinical, cognitive, biological, and behavioral) achieved the best performance. Although performance of the different models still have room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

scholarly journals Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model

2021 ◽  
Author(s):  
Andres Bustamante-Arias ◽  
Abbas Cheddad ◽  
Julio Cesar Jimenez-Perez ◽  
Alejandro Rodriguez-Garcia
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Transfer Learning ◽  
Digital Image ◽  
Binary Classification ◽  
Strong Predictor ◽  
Support Vector ◽  
Control Group ◽  
Group A ◽  
Sd Oct

Machine learning (ML) has a large capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied; three approaches of ML were used. Once all images were analyzed, representative areas from every digital image were also processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning - support vector machine (TL-SVM) (AUROC = 0.94, SPE 88%, SEN 100%) and transfer learning – random forest (TL- RF) method (AUROC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUROC = 0.84, SPE 77%, SEN 91%) and random forest (AUROC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas.

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