LIVIVO - Search results -

Search results

Result 1 - 10 of total 52

Search options

Article ; Online: ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection.

Attallah, Omneya

Biomimetics (Basel, Switzerland)

2024 Volume 9, Issue 3

Abstract: The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are ... ...

Abstract	The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled "ADHD-AID". The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time-frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification's complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID's results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters.
Language	English
Publishing date	2024-03-20
Publishing country	Switzerland
Document type	Journal Article
ISSN	2313-7673
ISSN (online)	2313-7673
DOI	10.3390/biomimetics9030188
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.

Attallah, Omneya

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 2

Abstract: One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, ... ...

Abstract	One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, accurate, and low-cost diagnosis of ROP. All previous CAD tools for ROP diagnosis use the original fundus images. Unfortunately, learning the discriminative representation from ROP-related fundus images is difficult. Textural analysis techniques, such as Gabor wavelets (GW), can demonstrate significant texture information that can help artificial intelligence (AI) based models to improve diagnostic accuracy. In this paper, an effective and automated CAD tool, namely GabROP, based on GW and multiple deep learning (DL) models is proposed. Initially, GabROP analyzes fundus images using GW and generates several sets of GW images. Next, these sets of images are used to train three convolutional neural networks (CNNs) models independently. Additionally, the actual fundus pictures are used to build these networks. Using the discrete wavelet transform (DWT), texture features retrieved from every CNN trained with various sets of GW images are combined to create a textural-spectral-temporal demonstration. Afterward, for each CNN, these features are concatenated with spatial deep features obtained from the original fundus images. Finally, the previous concatenated features of all three CNN are incorporated using the discrete cosine transform (DCT) to lessen the size of features caused by the fusion process. The outcomes of GabROP show that it is accurate and efficient for ophthalmologists. Additionally, the effectiveness of GabROP is compared to recently developed ROP diagnostic techniques. Due to GabROP's superior performance compared to competing tools, ophthalmologists may be able to identify ROP more reliably and precisely, which could result in a reduction in diagnostic effort and examination time.
Language	English
Publishing date	2023-01-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13020171
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article: MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning.

Attallah, Omneya

Digital health

2023 Volume 9, Page(s) 20552076231180054

Abstract: Objective: Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination ... ...

Abstract	Objective: Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named "Monkey-CAD" that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately. Methods: Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features' sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers. Results: Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively. Conclusions: Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.
Language	English
Publishing date	2023-06-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819396-9
ISSN	2055-2076
ISSN	2055-2076
DOI	10.1177/20552076231180054
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article: RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.

Attallah, Omneya

Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

2023 Volume 233, Page(s) 104750

Abstract: Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of ... ...

Abstract	Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.
Language	English
Publishing date	2023-01-02
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	55877-1
ISSN	0169-7439
ISSN	0169-7439
DOI	10.1016/j.chemolab.2022.104750
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 2595: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection.

Attallah, Omneya

Biomimetics (Basel, Switzerland)

2023 Volume 8, Issue 5

Abstract: Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential to recognize them quickly and accurately for prevention and control. Deep learning (DL)-based computer-assisted expert systems are encouraging approaches to ... ...

Abstract	Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential to recognize them quickly and accurately for prevention and control. Deep learning (DL)-based computer-assisted expert systems are encouraging approaches to solving this issue and dealing with the dearth of subject-matter specialists in this area. Nonetheless, a major generalization obstacle is posed by the existence of small discrepancies between various classes of paddy diseases. Numerous studies have used features taken from a single deep layer of an individual complex DL construction with many deep layers and parameters. All of them have relied on spatial knowledge only to learn their recognition models trained with a large number of features. This study suggests a pipeline called "RiPa-Net" based on three lightweight CNNs that can identify and categorize nine paddy diseases as well as healthy paddy. The suggested pipeline gathers features from two different layers of each of the CNNs. Moreover, the suggested method additionally applies the dual-tree complex wavelet transform (DTCWT) to the deep features of the first layer to obtain spectral-temporal information. Additionally, it incorporates the deep features of the first layer of the three CNNs using principal component analysis (PCA) and discrete cosine transform (DCT) transformation methods, which reduce the dimension of the first layer features. The second layer's spatial deep features are then combined with these fused time-frequency deep features. After that, a feature selection process is introduced to reduce the size of the feature vector and choose only those features that have a significant impact on the recognition process, thereby further reducing recognition complexity. According to the results, combining deep features from two layers of different lightweight CNNs can improve recognition accuracy. Performance also improves as a result of the acquired spatial-spectral-temporal information used to learn models. Using 300 features, the cubic support vector machine (SVM) achieves an outstanding accuracy of 97.5%. The competitive ability of the suggested pipeline is confirmed by a comparison of the experimental results with findings from previously conducted research on the recognition of paddy diseases.
Language	English
Publishing date	2023-09-07
Publishing country	Switzerland
Document type	Journal Article
ISSN	2313-7673
ISSN (online)	2313-7673
DOI	10.3390/biomimetics8050417
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: MonDiaL-CAD

Omneya Attallah

Digital Health, Vol

Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

2023 Volume 9

Abstract: Objective Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of ...

Abstract	Objective Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named “Monkey-CAD” that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately. Methods Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features’ sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers. Results Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively. Conclusions Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	004
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

Omneya Attallah

Applied Sciences, Vol 13, Iss 1916, p

2023 Volume 1916

Abstract: Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for the timely identification of cervical cancer, but it is susceptible to human ... ...

Abstract	Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for the timely identification of cervical cancer, but it is susceptible to human mistakes. Artificial Intelligence-reliant computer-aided diagnostic (CAD) methods have been extensively explored to identify cervical cancer in order to enhance the conventional testing procedure. In order to attain remarkable classification results, most current CAD systems require pre-segmentation steps for the extraction of cervical cells from a pap smear slide, which is a complicated task. Furthermore, some CAD models use only hand-crafted feature extraction methods which cannot guarantee the sufficiency of classification phases. In addition, if there are few data samples, such as in cervical cell datasets, the use of deep learning (DL) alone is not the perfect choice. In addition, most existing CAD systems obtain attributes from one domain, but the integration of features from multiple domains usually increases performance. Hence, this article presents a CAD model based on extracting features from multiple domains not only one domain. It does not require a pre-segmentation process thus it is less complex than existing methods. It employs three compact DL models to obtain high-level spatial deep features rather than utilizing an individual DL model with large number of parameters and layers as used in current CADs. Moreover, it retrieves several statistical and textural descriptors from multiple domains including spatial and time–frequency domains instead of employing features from a single domain to demonstrate a clearer representation of cervical cancer features, which is not the case in most existing CADs. It examines the influence of each set of handcrafted attributes on diagnostic accuracy independently and hybrid. It then examines the consequences of combining each DL feature set obtained from each CNN with the combined handcrafted features. Finally, it uses ...
Keywords	cervical cancer ; pap smear test ; discrete wavelet transform ; grey-level co-occurrence matrix ; gabor wavelet transform ; convolutional neural networks ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	004
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: A deep learning-based diagnostic tool for identifying various diseases via facial images.

Attallah, Omneya

Digital health

2022 Volume 8, Page(s) 20552076221124432

Abstract: With the current health crisis caused by the COVID-19 pandemic, patients have become more anxious about infection, so they prefer not to have direct contact with doctors or clinicians. Lately, medical scientists have confirmed that several diseases ... ...

Abstract	With the current health crisis caused by the COVID-19 pandemic, patients have become more anxious about infection, so they prefer not to have direct contact with doctors or clinicians. Lately, medical scientists have confirmed that several diseases exhibit corresponding specific features on the face the face. Recent studies have indicated that computer-aided facial diagnosis can be a promising tool for the automatic diagnosis and screening of diseases from facial images. However, few of these studies used deep learning (DL) techniques. Most of them focused on detecting a single disease, using handcrafted feature extraction methods and conventional machine learning techniques based on individual classifiers trained on small and private datasets using images taken from a controlled environment. This study proposes a novel computer-aided facial diagnosis system called FaceDisNet that uses a new public dataset based on images taken from an unconstrained environment and could be employed for forthcoming comparisons. It detects single and multiple diseases. FaceDisNet is constructed by integrating several spatial deep features from convolutional neural networks of various architectures. It does not depend only on spatial features but also extracts spatial-spectral features. FaceDisNet searches for the fused spatial-spectral feature set that has the greatest impact on the classification. It employs two feature selection techniques to reduce the large dimension of features resulting from feature fusion. Finally, it builds an ensemble classifier based on stacking to perform classification. The performance of FaceDisNet verifies its ability to diagnose single and multiple diseases. FaceDisNet achieved a maximum accuracy of 98.57% and 98% after the ensemble classification and feature selection steps for binary and multiclass classification categories. These results prove that FaceDisNet is a reliable tool and could be employed to avoid the difficulties and complications of manual diagnosis. Also, it can help physicians achieve accurate diagnoses without the need for physical contact with the patients.
Language	English
Publishing date	2022-09-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819396-9
ISSN	2055-2076
ISSN	2055-2076
DOI	10.1177/20552076221124432
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.

Attallah, Omneya

Biosensors

2022 Volume 12, Issue 5

Abstract: Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel ... ...

Abstract	Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.
MeSH term(s)	Algorithms ; COVID-19/diagnosis ; Communicable Disease Control ; Deep Learning ; Electrocardiography ; Humans
Language	English
Publishing date	2022-05-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662125-3
ISSN	2079-6374 ; 2079-6374
ISSN (online)	2079-6374
ISSN	2079-6374
DOI	10.3390/bios12050299
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.

Attallah, Omneya

Computers in biology and medicine

2022 Volume 142, Page(s) 105210

Abstract: The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several ... ...

Abstract	The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) applications, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.
MeSH term(s)	COVID-19 ; COVID-19 Testing ; Communicable Disease Control ; Electrocardiography ; Humans ; Neural Networks, Computer ; SARS-CoV-2
Language	English
Publishing date	2022-01-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.105210
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 653: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

To top

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito