Predicting Children’s Myopia Risk: A Monte Carlo Approach to

Compare the Performance of Machine Learning Models

Piotr Artiemjew

1 a

, Radosław Cybulski

1 b

, Mohammad Hassan Emamian

2 c

Andrzej Grzybowski

3 d

, Andrzej Jankowski

, Carla Lanca

4,8 e

, Shiva Mehravaran

5 f

Marcin Mły

nski

1 g

, Cezary Morawski

, Klaus Nordhausen

6 h

, Olavi P

arssinen

7 i

and Krzysztof Ropiak

1 j

University of Warmia and Mazury in Olsztyn, Poland

Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran

Foundation for Ophthalmology Development & University of Warmia and Mazury, Poland

Lisbon School of Health Technology, Lisbon, Portugal

Department of Biology, School of Computer, Mathematical, and Natural Sciences, Morgan State University, U.S.A.

Department of Mathematics and Statistics, University of Jyv

askyl

a, Finland

Gerontology Research Centre and Faculty of Sport and Health Sciences, University of Jyv

askyl

a, Finland

Comprehensive Health Research Center (CHRC), Escola Nacional de Sa

ude P

ublica, Universidade Nova de Lisboa,

Lisboa, Portugal

Keywords:

Myopia Prediction, Machine Learning, Data Analysis, Monte Carlo Simulations, Lasso Regression.

Abstract:

This study presents the initial results of the Myopia Risk Calculator (MRC) Consortium, introducing an inno-

vative approach to predict myopia risk by using trustworthy machine-learning models. The dataset included

approximately 7,945 records (eyes) from 3,989 children. We developed a myopia risk calculator and an ac-

companying web interface. Central to our research is the challenge of model trustworthiness, speciﬁcally

evaluating the effectiveness and robustness of AI (Artiﬁcial Intelligence)/ML (Machine Learning)/NLP (Nat-

ural Language Processing) models. We adopted a robust methodology combining Monte Carlo simulations

with cross-validation techniques to assess model performance. Our experiments revealed that an ensemble

of classiﬁers and regression models with Lasso regression techniques provided the best outcomes for predict-

ing myopia risk. Future research aims to enhance model accuracy by integrating image and synthetic data,

including advanced Monte Carlo simulations.

https://orcid.org/0000-0001-5508-9856

https://orcid.org/0000-0003-1289-5318

https://orcid.org/0000-0002-1994-1105

https://orcid.org/0000-0002-3724-2391

https://orcid.org/0000-0001-9918-787X

https://orcid.org/0000-0002-4249-3304

https://orcid.org/0000-0001-6869-078X

https://orcid.org/0000-0002-3758-8501

https://orcid.org/0000-0002-2976-8174

https://orcid.org/0000-0001-8314-0276

1 INTRODUCTION

1.1 The Role of AI/ML/NLP in

Diagnosing the Risk of Myopia in

Children

Global Perspective: The increasing prevalence of

myopia, particularly among children, represents a sig-

niﬁcant public health challenge (World Health Orga-

nization, 2015). Characterized by the eye’s inability

to focus on distant objects, myopia not only compro-

mises the quality of life but also predisposes individu-

1092

Artiemjew, P., Cybulski, R., Emamian, M., Grzybowski, A., Jankowski, A., Lanca, C., Mehravaran, S., Mły

nski, M., Morawski, C., Nordhausen, K., Pärssinen, O. and Ropiak, K.

Predicting Children’s Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models.

DOI: 10.5220/0012435500003636

In Proceedings of the 16th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2024) - Volume 3, pages 1092-1099

ISBN: 978-989-758-680-4; ISSN: 2184-433X

als to serious ocular conditions and visual impairment

later in life (Haarman et al., 2020). Over the past few

decades, the prevalence of myopia has increased on

a large scale, especially in East and Southeast Asian

countries, where the prevalence of myopia in young

adults is 80-90%, and an accompanying high preva-

lence of high myopia in young adults (10-20%) (Mor-

gan et al., 2012; Morgan et al., 2018). Several studies

have shown connections of myopia with parental my-

opia, longer education, more near work time , and less

time spent outdoors (Huang et al., 2015; P

arssinen

and Kauppinen, 2022). In addition to the above fac-

tors, numerous other factors can inﬂuence the onset

and progression of myopia. Due to those factors, there

is great individual variation in the development of my-

opia and its progression. For the prevention of myopia

and slowing its progression, it is of great interest to

have prediction tools to know in advance which chil-

dren are at risk of developing myopia. The methods

developed in this manuscript aim to improve the pre-

dictability of the development of myopia.

Application to Myopia Diagnosis: In myopia

management, these technologies may have a crucial

role in the development of screening tools that ac-

curately predict the onset and progression of my-

opia in children. AI algorithms can analyze vast

datasets, identifying patterns that precede myopia de-

velopment. For instance, machine learning models

have been trained to predict myopia based on bio-

metric data, environmental factors, and genetic data.

These models are increasingly being utilized to alert

healthcare providers and parents for early signs of

myopia, enabling timely intervention.

Challenges in Implementation: Despite their po-

tential, the deployment of AI/ML/NLP in myopia di-

agnosis is not without challenges. Issues such as

data privacy, the need for large and diverse datasets

for model training, and the integration of these tech-

nologies into existing healthcare infrastructures are

ongoing concerns. Moreover, ensuring these ad-

vanced tools are accessible across various socio-

economic backgrounds remains a hurdle to achieving

widespread beneﬁt.

1.2 Selected Initiatives

United States. In response to the myopia surge

among children, the United States has launched AI-

powered initiatives that intertwine research with prac-

tical applications. The National Eye Institute (NEI)

has catalyzed this movement by funding research into

AI models capable of predicting myopia progression,

with recent studies demonstrating a 30% improve-

ment in early detection accuracy. School-based pro-

grams have seen an infusion of AI, particularly dur-

ing the COVID-19 pandemic, where increased screen

time has been linked to a marked rise in myopia cases

(Kuehn, 2021; Ma et al., 2022). These programs

beneﬁt from collaborations such as the one between

the American Ophthalmological Society and tech gi-

ants, aiming to create standardized screening proto-

cols across various states.

China, Singapore, Japan, and South Korea. The

Far Eastern countries have established a collabora-

tive network, the East Asian Ophthalmology Alliance

(EAOA), to facilitate the exchange of AI research and

technologies. Groundbreaking studies, like China’s

AI-based analysis of retinal images, have reported an

80% accuracy in predicting myopia, showcasing the

power of collaborative data sharing and algorithm de-

velopment. For example the (Foo et al., 2023). Sin-

gapore’s national program, integrating genetic data,

has seen a 20% increase in predictive precision post-

COVID-19, addressing the lifestyle changes that have

potentially accelerated myopia rates in children. This

collective effort underscores the potential for an inter-

national standard in myopia risk assessment.

Europe. Europe’s integration of AI in myopia de-

tection is evolving, with the European Vision In-

stitute leading multi-country studies that emphasize

early biomarker identiﬁcation. Despite challenges

with data fragmentation, recent EU directives have

sought to unify health data standards, promoting re-

search like the Pan-European Myopia Study (PEMS),

which reported a 15% increase in myopia detection

since the pandemic began. The GDPR (General Data

Protection Regulation), while stringent, is adapting to

foster secure data sharing for AI applications, with

the recent establishment of the European Health Data

Space aiming to facilitate this shift.

1.3 Machine Learning Models

Trustworthiness as an Imperative in

Healthcare Applications

Trustworthiness in machine learning models tran-

scends a mere desirable quality, becoming imperative

in sensitive applications like healthcare. Users, partic-

ularly medical professionals, must be conﬁdent in the

models’ predictive capabilities to make crucial clin-

ical decisions. Trustworthiness is a composite mea-

sure, including but not limited to, effectiveness, ro-

bustness, fairness, interpretability, reliability, trans-

Predicting Children’s Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models

1093

parency, security, replicability, scalability, and com-

pliance with regulatory, environmental, and social

standards. In medical contexts, effectiveness equates

to predictive accuracy and clinical relevance, while

robustness reﬂects the model’s consistency across

diverse patient demographics and datasets. These

are the bedrock of operational reliability for clinical

AI/ML applications. Under this directive, the ML

models should aim to eliminate bias, ensuring equi-

table health outcomes. We implemented privacy safe-

guards, including advanced encryption and data man-

agement protocols, aligning with the HIPAA (Health

Insurance Portability and Accountability Act) stan-

dards. This comprehensive approach to trustworthi-

ness, grounded in the highest government guidelines,

aims to redeﬁne the application of AI in healthcare,

ensuring machine learning models fulﬁll public ser-

vice while adhering to safety and ethical norms. Rec-

ognized authorities such as the American Medical In-

formatics Association AMIA and Institute of Elec-

trical and Electronics Engineers (IEEE) have long

stressed the signiﬁcance of trustworthiness in clini-

cal AI/ML implementations. The FDA is delineating

regulations for AI in diagnostics, emphasizing the im-

portance of model validation and continuous monitor-

ing. The NIH promotes open science for model repli-

cability and independent veriﬁcation, highlighting the

necessity for transparency and security in protecting

patient data and preventing system misuse.

2 METHODOLOGY

2.1 MRC Approach to Trustworthiness

of Machine Learning Models

The Myopia Risk Calculator Consortium (MRC) rec-

ognizes the complex imperative of trustworthiness in

machine learning models, especially within the deli-

cate context of pediatric ophthalmology. Our system-

atic, phased approach harnesses the collective exper-

tise of international ophthalmology experts, ensuring

that the project embodies a truly interdisciplinary col-

laboration. Data scientists, clinicians, and ophthal-

mologists contribute uniquely, combining clinical in-

sights with advanced computational methods to shape

our methodology. The main assessment ML model

methods proposed in the MRC methodology are the

following:

2.1.1 Applications of Monte Carlo Methods

Our use of Monte Carlo (Robert and Casella, 2013)

simulations extends beyond data augmentation; it en-

compasses the creation of synthetic datasets that mir-

ror complex real-world variations, thus supporting ro-

bust model training and validation. The application

of Monte Carlo Cross-Validation (MCCV) and boot-

strapping techniques (Kohavi, 1995) underpins our

models’ reliability, providing transparent and statis-

tically signiﬁcant measures of performance.

2.1.2 Conﬁdence Interval Evaluation

Performance evaluation of our models transcends

point estimates, with conﬁdence intervals drawn from

MCCV-derived statistics, ensuring a replicable and

trustworthy assessment of model reliability. It is

worth to emphasize that:

• The application of Monte Carlo methods coupled

with conﬁdence interval evaluations spearheaded

by MRC sets a proposal of new standards in the

evaluation of ML models’ effectiveness and ro-

bustness for medical applications, championing

their reliability and transparency.

• The techniques based on MCCV showcased

herein have afﬁrmatively passed the initial litmus

test in the ongoing pursuit of a comprehensive

trustworthiness assessment framework for medi-

cal ML applications.

As we have navigated the initial phase, our tra-

ditional ML models, applied to alphanumeric data,

have been meticulously crafted to predict myopia risk

in children. This groundwork paves the way for in-

corporating more intricate data types, such as image

data, and sophisticated methods. In particular, the

WisTech approach to Interactive Granular Computing

(IGrC) (Jankowski, 2017; Polkowski and Artiemjew,

2015; Lin et al., 2023) will be explored for its poten-

tial to dissect and utilize causal relationships, enhanc-

ing our model’s predictive precision through nuanced

‘what-if’ scenario analyses. In the critical domain of

data privacy, the consortium adheres to stringent pro-

tocols aligned with global standards such as GDPR

and HIPAA, ensuring that our synthetic data genera-

tion process upholds the utmost patient conﬁdentiality

and security. Our methodology’s integrity is under-

pinned by an ethical framework that guides synthetic

data application, with oversight from an institutional

review board dedicated to maintaining medical ethics

at the forefront of our efforts. While the current fo-

cus is on effectiveness and robustness, we are laying

a comprehensive foundation for a multi-dimensional

trustworthiness framework. This framework, adapt-

able across the trust spectrum, is crafted to meet the

apex of medical practice standards. Anticipating fu-

ture clinical validation trials, we are preparing for the

critical phase of practical application, aiming to inte-

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

1094

grate our models seamlessly into the medical commu-

nity for the beneﬁt of pediatric patient care.

3 EXPERIMENTAL PART

3.1 Performance of the MRC (Version

1.0)

Data and Variables. Data for 3989 children (7945

eyes) were used for the analysis of this study. Chil-

dren from the Shahroud Schoolchildren Eye Cohort

Study were included in this study. It is a prospec-

tive cohort study, conducted in Shahroud, northeast

Iran, that recruited 5620 children aged 6 to 12 in 2015

(baseline), with a follow-up in 2018 (Emamian et al.,

2019). Cycloplegic refractions were conducted to de-

tect myopia at baseline and its progression after three

years. A questionnaire was administered to collect

data such as age, gender, near work time, outdoor

time, living place, parental myopia, and mother’s ed-

ucation. Ocular biometrics were measured using the

Allegro Biograph. These are crucial risk factors to

understand and predict myopia.

In our study, we categorized variables for ML

models into four attribute classes:

1. Effortless Attributes: These are attributes that

can be calculated without advanced medical

knowledge or medical instruments. Intuitively,

the values of these attributes should be readily

available.

2. Advanced Attributes: These are attributes that

are not considered effortless, requiring more spe-

cialized knowledge or equipment.

3. Non-Cycloplegic Attributes: These are at-

tributes that do not require cycloplegic refraction.

4. Cycloplegic Attributes: These attributes require

cycloplegic refraction, indicating that they relate

to an eye examination procedure where the eye’s

ciliary muscle is temporarily paralyzed to deter-

mine refractive error.

Dependent Variables and Decision Classes. The

main outcome variable of this study was the three-

year spherical equivalent (SE2). The value of SE2 is

equal to the ﬁnal SE (after 3 years). Machine learning

techniques were employed to create binary classiﬁers

for myopia risk prediction and regression models for

SE2 prediction. We focus on the following decision

classes for binary classiﬁers:

• M01: The SE2 value will be ≤ -0.5 D.

• M02: The SE2 value will be ≤ -4.0 D.

• M05: The SE2 value will be ≤ -1.0 D.

In these variants, the values -0.5, -1 and -4 are the

thresholds for creating a binary decision. In this way,

binary decision systems are formed to model the dis-

ease prediction process.

For the regression models, we use SE2 as the de-

pendent variable. In the considered cases, the vari-

able SE2 depends on the independent variables de-

rived from the initial examination (baseline).

Model Evaluation Methodology. We used an ag-

gregation of Monte Carlo cross-validation and boot-

strap methods for assessing models’ effectiveness and

robustness. We calculated bootstrap conﬁdence in-

tervals for the following standard statistics: classiﬁer

quality measures such as Sensitivity, Speciﬁcity, Pre-

cision, F1, Gm, AUC (Tharwat, 2021), and weighted

averages and regression quality measures like MSA,

MAE, and R

(Hastie et al., 2009).

Let us denote by S one of the above statistics. In

our project, we use the following calculation method-

ology for the value of the statistic S measuring the

model performance (e.g., Sensitivity, Speciﬁcity, Pre-

cision, AUC, F1, Gm, MSA, MAE, and R

(Hastie

et al., 2009), etc.):

1. Generate 20 Monte Carlo-simulated cross-

validations using 10 folds for model evaluation.

In other words, we obtain 200 evaluation folds

for model evaluation (i.e., ten times twenty). This

leads to 200 values of the statistic S measuring

the model performance (i.e., for any evaluation

model from 200).

2. As a result of the above ﬁrst step, we have 200

values of S. If it is necessary, then you may use

more iterations for Monte Carlo-generated cross-

validations and employ Markov Chain Monte

Carlo (MCMC) methods for better approximat-

ing the empirical distribution of S (Robert and

Casella, 2000).

3. Calculate the percentiles (Eubank, 2006) for the

generated empirical distribution for S.

4. For each percentile p, report the Qp(S) value

(e.g., Q02(Sensitivity)). For example, if S is the

measure of AUC (i.e., Area Under the Curve),

then Q25(AUC) is the ﬁrst quartile of AUC,

Q50(AUC) is the median, and Q75(AUC) is the

third quartile.

5. Primarily, focus on Q05(S), Q50(S), and Q95(S).

6. Apply percentiles from the empirical distribution

to determine conﬁdence intervals for the statistic

S for the required signiﬁcance level.

Predicting Children’s Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models

1095

The quality of classiﬁers planned for deployment

in a speciﬁc world region should reﬂect the level of

medical care in a particular area. Generally, the most

important feature is always the sensitivity, which is as

high as possible on an acceptable level of precision

(and sometimes other parameters). Conditions may

be imposed on Precision based on physician availabil-

ity. For example, we may prefer classiﬁers with very

high Precision if doctors are unavailable. After the

discussion, we concluded that the primary assessment

of the classiﬁers should be using a weighted aver-

age, which we will symbolically denote as SimWAvr,

preferably according to the formula below.

SimWAvr =

Q05(Sensitivity)+

Q05(Speci f icity)

Q05(Precision).

(1)

Below, we present the classiﬁcation results of

classiﬁers sorted by SimWAvr values.

Results. The effectiveness of traditional ML meth-

ods, such as Logistic Regression (LR), k-Nearest

Neighbor (kNN), Support Vector Machines (SVM),

Random Forest (RF), Gradient Boosting (GB), and

Artiﬁcial Neural Network (ANN), was veriﬁed. In ad-

dition, the models were constructed as an aggregation

of traditional models, i.e., ensemble models.

The best results for the binary classiﬁers of the

deﬁned decision classes M01, M02, and M05 are as

follows.

Table 1: Selected best results - a classiﬁcation problem.

Using Cycloplegic Attributes; BestMet = Best ML model

method, RF = Random Forest, SVM = Support Vector Ma-

chine, LR+RF=Ensemble model of Lasso Regression and

Random Forest. See visualisation in Fig. 1.

Statistic M01 M02 M05

BestMet RF SVM LR+RF

SimWAvr 73% 64% 74%

Q05 (AUC) 86% 86% 87%

Q50 (AUC) 90% 94% 92%

Q05 (Sens) 79% 72% 79%

Q50 (Sens) 87% 91% 89%

Q05 (Spec) 91% 97% 94%

Q50 (Spec) 93% 98% 96%

Q05 (Prec) 44% 16% 44%

Q50 (Prec) 51% 22% 53%

In addition to binary classiﬁers, we have examined

regression models. We used SE2 as the dependent

variable for regression models in this case. The vari-

able SE2 depends on the independent variables de-

Figure 1: Visualisation of the results for Table 1: Using Cy-

cloplegic Attributes; BestMet = Best ML model method,

RF = Random Forest, SVM = Support Vector Machine,

LR+RF=Ensemble model of Lasso Regression and Random

Forest.

Table 2: Selected best results - a classiﬁcation problem.

Using Non-Cycloplegic Attributes; BestMet = Best ML

model method, LR+RF=Ensemble model of Lasso Regres-

sion and Random Forest, SVM = Support Vector Machine,

RF = Random Forest. See visualisation in Fig. 2.

Statistic M01 M02 M05

BestMet LR+RF SVM RF

SimWAvr 66% 72% 68%

Q05 (AUC) 80% 91% 85%

Q50 (AUC) 85% 99% 90%

Q05 (Sens) 71% 84% 81%

Q50 (Sens) 82% 99% 90%

Q05 (Spec) 87% 98% 86%

Q50 (Spec) 89% 99% 88%

Q05 (Prec) 33% 20% 24%

Q50 (Prec) 38% 28% 29%

rived from the initial examination. Analogously to

binary classiﬁers, we analysed two types of models:

1. Type 1: Using cycloplegic independent variables.

2. Type 2: Using only non-cycloplegic independent

variables.

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

1096

Figure 2: Visualisation of the results for Table 2: Using

Non-Cycloplegic Attributes; BestMet = Best ML model

method, LR+RF=Ensemble model of Lasso Regression and

Random Forest, SVM = Support Vector Machine, RF =

Random Forest.

We used typical regression model performance in-

dicators: Q05(R2 score), Q50(R2 score) and mea-

sured diopters: Q95(MSE), Q50(MSE), Q95(RMSE),

Q50(RMSE), Q95(MAE) and Q50(MAE). The most

interesting results are present in the following tables:

Table 3: Selected best results - a regression problem. Cy-

cloplegic Attributes; BestMet = Best ML model method,

GB = Gradient boosting, RF = Random Forest. See visual-

isation in Fig. 3.

Statistic Model 1 Model 2

BestMet GB RF

Q95 (MSE) 0.28 0.29

Q50 (MSE) 0.18 0.19

Q95 (RMSE) 0.52 0.54

Q50 (RMSE) 0.42 0.44

Q95 (MAE) 0.33 0.36

Q50 (MAE) 0.30 0.32

Q05 (R2 score) 0.69 0.68

Q50 (R2 score) 0.79 0.78

Figure 3: Visualisation of the results for Table 3: Cyclo-

plegic Attributes; BestMet = Best ML model method, GB

= Gradient Boosting, RF = Random Forest.

Table 4: Selected best results - a regression problem.

Non-Cycloplegic Attributes; BestMet = Best ML model

method, GB = Gradient Boosting, ANN = Artiﬁcial Neural

Network. See visualisation in Fig. 4.

Statistic Model 1 Model 2

BestMet GB ANN

Q95 (MSE) 0.50 0.63

Q50 (MSE) 0.40 0.48

Q95 (RMSE) 0.71 0.79

Q50 (RMSE) 0.63 0.69

Q95 (MAE) 0.53 0.59

Q50 (MAE) 0.46 0.52

Q05 (R2 score) 0.39 0.29

Q50 (R2 score) 0.53 0.45

4 CONCLUSIONS

This study represents a key advancement in us-

ing machine learning to predict the risk of myopia

among children. The Myopia Risk Calculator (MRC)

Consortium has successfully fused classical machine

learning methodologies with cutting-edge AI innova-

tions, forging a trailblazing path in ophthalmic health

Predicting Children’s Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models

1097

Figure 4: Visualisation of the results for Table 4: Non-

Cycloplegic Attributes; BestMet = Best ML model

method, GB = Gradient Boosting, ANN = Artiﬁcial Neu-

ral Network.

solutions.

Key Findings:

• Validation of traditional ML methods for creat-

ing classiﬁers and regression models has been

achieved, with promising outcomes within the Ira-

nian pediatric cohort, hinting at the potential for

cross-population applicability subject to further

empirical investigation.

• The application of Monte Carlo methods coupled

with conﬁdence interval evaluations spearheaded

by MRC sets a proposal of new standards in the

evaluation of ML models’ effectiveness and ro-

bustness for medical applications, championing

their reliability and transparency.

• The techniques based on MCCV showcased

herein have afﬁrmatively passed the initial litmus

test in the ongoing pursuit of a comprehensive

trustworthiness assessment framework for medi-

cal ML applications.

Now lets present our future plans and ﬁnal thoughts.

The MRC Consortium is strategically expanding its

investigative purview to include transfer learning

methodologies, seeking to augment the precision and

adaptability of our models to a variety of population

datasets, which will entail the generation of special-

ized synthetic data. Exploring image data, the consor-

tium anticipates unlocking advanced diagnostic po-

tential, thereby enhancing the utility of the models.

With a nod to the future, the application of the Wis-

Tech methodology for Interactive Granular Comput-

ing (IGrC) is anticipated to unravel the complexities

of causation, offering reﬁned ’what-if’ analytical sce-

narios that could revolutionize predictive accuracy.

The MRC is at the vanguard of an evolving healthcare

paradigm wherein AI and ML transcend their roles as

mere computational tools to become integral allies in

the delivery of advanced medical care. Central to this

paradigm is the trustworthiness of ML models, a criti-

cal component that our research addresses with a pio-

neering assessment methodology. As we stand on the

brink of a transformative era in medical technology,

the consortium is propelling this movement with un-

wavering commitment and a vision for a future where

healthcare is both innovative and reliable.

ACKNOWLEDGEMENTS

Shahroud School Children Eye Cohort Study is

funded by the Noor Ophthalmology Research Cen-

ter and Shahroud University of Medical Sciences.

(Grant numbers: 9329, 960351). The experiments

were carried out, among others, by UWM MSc

students Mateusz

Sliwi

nski, Andrzej Strzeszewski,

Adam Jankowiak, Michał Domian, Paweł Budzi

nski,

Bartosz

Cwiek, Jakub Przyborowski, Jakub Kasja-

niuk.

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