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  1. Article ; Online: Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

    Charis Ntakolia / Christos Kokkotis / Serafeim Moustakidis / Dimitrios Tsaopoulos

    Diagnostics, Vol 11, Iss 2, p

    2021  Volume 285

    Abstract: Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two ... ...

    Abstract Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features’ impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.
    Keywords machine learning ; knee osteoarthritis ; joint space narrowing prediction ; feature selection ; interpretation ; Medicine (General) ; R5-920
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Leveraging explainable machine learning to identify gait biomechanical parameters associated with anterior cruciate ligament injury

    Christos Kokkotis / Serafeim Moustakidis / Themistoklis Tsatalas / Charis Ntakolia / Georgios Chalatsis / Stylianos Konstadakos / Michael E. Hantes / Giannis Giakas / Dimitrios Tsaopoulos

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 12

    Abstract: Abstract Anterior cruciate ligament (ACL) deficient and reconstructed knees display altered biomechanics during gait. Identifying significant gait changes is important for understanding normal and ACL function and is typically performed by statistical ... ...

    Abstract Abstract Anterior cruciate ligament (ACL) deficient and reconstructed knees display altered biomechanics during gait. Identifying significant gait changes is important for understanding normal and ACL function and is typically performed by statistical approaches. This paper focuses on the development of an explainable machine learning (ML) empowered methodology to: (i) identify important gait kinematic, kinetic parameters and quantify their contribution in the diagnosis of ACL injury and (ii) investigate the differences in sagittal plane kinematics and kinetics of the gait cycle between ACL deficient, ACL reconstructed and healthy individuals. For this aim, an extensive experimental setup was designed in which three-dimensional ground reaction forces and sagittal plane kinematic as well as kinetic parameters were collected from 151 subjects. The effectiveness of the proposed methodology was evaluated using a comparative analysis with eight well-known classifiers. Support Vector Machines were proved to be the best performing model (accuracy of 94.95%) on a group of 21 selected biomechanical parameters. Neural Networks accomplished the second best performance (92.89%). A state-of-the-art explainability analysis based on SHapley Additive exPlanations (SHAP) and conventional statistical analysis were then employed to quantify the contribution of the input biomechanical parameters in the diagnosis of ACL injury. Features, that would have been neglected by the traditional statistical analysis, were identified as contributing parameters having significant impact on the ML model’s output for ACL injury during gait.
    Keywords Medicine ; R ; Science ; Q
    Subject code 796
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine

    Charis Ntakolia / Dimitrios E. Diamantis / Nikolaos Papandrianos / Serafeim Moustakidis / Elpiniki I. Papageorgiou

    Healthcare, Vol 8, Iss 493, p

    A Case Study on Prostate Cancer Patients

    2020  Volume 493

    Abstract: Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots ... ...

    Abstract Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment.
    Keywords machine learning ; convolutional neural network ; bone metastasis classification ; deep learning ; medical image ; nuclear medicine ; Medicine ; R
    Subject code 006
    Language English
    Publishing date 2020-11-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Explainable AI-Based Identification of Contributing Factors to the Mood State Change in Children and Adolescents with Pre-Existing Psychiatric Disorders in the Context of COVID-19-Related Lockdowns in Greece

    Charis Ntakolia / Dimitrios Priftis / Konstantinos Kotsis / Konstantina Magklara / Mariana Charakopoulou-Travlou / Ioanna Rannou / Konstantina Ladopoulou / Iouliani Koullourou / Emmanouil Tsalamanios / Eleni Lazaratou / Aspasia Serdari / Aliki Grigoriadou / Neda Sadeghi / Kenny Chiu / Ioanna Giannopoulou

    BioMedInformatics, Vol 3, Iss 4, Pp 1040-

    2023  Volume 1059

    Abstract: The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards ... ...

    Abstract The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards the impact of the elongation of COVID-19-related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies focus on individuals, such as students, adults, and youths, among others, with little attention being given to the elongation of COVID-19-related measures and their impact on a special group of individuals, such as children and adolescents with diagnosed developmental and psychiatric disorders. In addition, most of these studies adopt statistical methodologies to identify pair-wise relationships among factors, an approach that limits the ability to understand and interpret the impact of various factors. In response, this study aims to adopt an explainable machine learning approach to identify factors that explain the deterioration or amelioration of mood state in a youth clinical sample. The purpose of this study is to identify and interpret the impact of the greatest contributing features of mood state changes on the prediction output via an explainable machine learning pipeline. Among all the machine learning classifiers, the Random Forest model achieved the highest effectiveness, with 76% best AUC-ROC Score and 13 features. The explainability analysis showed that stress or positive changes derived from the imposing restrictions and COVID-19 pandemic are the top two factors that could affect mood state.
    Keywords COVID-19 pandemic ; mental health ; machine learning ; explainability ; children and adolescents ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 360
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Correction

    Charis Ntakolia / Dimitrios Priftis / Mariana Charakopoulou-Travlou / Ioanna Rannou / Konstantina Magklara / Ioanna Giannopoulou / Konstantinos Kotsis / Aspasia Serdari / Emmanouil Tsalamanios / Aliki Grigoriadou / Konstantina Ladopoulou / Iouliani Koullourou / Neda Sadeghi / Georgia O’Callaghan / Eleni Lazaratou

    Healthcare, Vol 10, Iss 657, p

    Ntakolia et al. An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece. Healthcare 2022, 10 , 149

    2022  Volume 657

    Abstract: Argyris Stringaris was initially included as an author in the original publication [.] ...

    Abstract Argyris Stringaris was initially included as an author in the original publication [.]
    Keywords n/a ; Medicine ; R
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  6. Article ; Online: An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

    Charis Ntakolia / Dimitrios Priftis / Mariana Charakopoulou-Travlou / Ioanna Rannou / Konstantina Magklara / Ioanna Giannopoulou / Konstantinos Kotsis / Aspasia Serdari / Emmanouil Tsalamanios / Aliki Grigoriadou / Konstantina Ladopoulou / Iouliani Koullourou / Neda Sadeghi / Georgia O’Callaghan / Argyris Stringaris / Eleni Lazaratou

    Healthcare, Vol 10, Iss 149, p

    2022  Volume 149

    Abstract: The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the ... ...

    Abstract The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.
    Keywords COVID-19 pandemic ; children and adolescents ; machine learning ; post hoc explainability ; model calibration ; Medicine ; R
    Subject code 360
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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