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Book: The surgical management of parasitic diseases

Tsoulfas, George / Hoballah, Jamal J. / Velmahos, George C. / Ho, Yik-Hong

2020

Author's details	George Tsoulfas, Jamal J. Hoballah, George C. Velmahos, Yik-Hong Ho editors
Keywords	Surgery ; Gastroenterology
Subject code	617
Language	English
Size	XVI, 370 Seiten, Illustrationen, Karten
Publisher	Springer International Publishing
Publishing place	Cham
Publishing country	Switzerland
Document type	Book
HBZ-ID	HT020556372
ISBN	978-3-030-47947-3 ; 9783030479480 ; 3-030-47947-1 ; 303047948X
Database	Catalogue ZB MED Medicine, Health

In stock of ZB MED Cologne/Königswinter

Shelf mark	Loan status	Location
2020 A 1136	available for loan (reading room)	Lesesaal EG

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Article ; Online: Editorial: Immersive Media in Connected Health.

Antoniou, Panagiotis E / Economou, Daphne / Athanasiou, Alkinoos / Tsoulfas, George

Frontiers in digital health

2021 Volume 3, Page(s) 697336

Language	English
Publishing date	2021-09-08
Publishing country	Switzerland
Document type	Editorial
ISSN	2673-253X
ISSN (online)	2673-253X
DOI	10.3389/fdgth.2021.697336
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluating trauma care, outcomes and costs in a system in crisis: the necessity of a Greek National Trauma Database.

Prionas, Apostolos / Tsoulfas, George / Tooulias, Andreas / Papakoulas, Apostolos / Piachas, Athanasios / Papadopoulos, Vasileios

Trauma surgery & acute care open

2020 Volume 5, Issue 1, Page(s) e000401

Abstract: Background: At present there is no organized trauma system in Greece and no national trauma database. The objective of this study was to record and evaluate trauma management at our university hospital and to measure the associated healthcare costs, ... ...

Abstract	Background: At present there is no organized trauma system in Greece and no national trauma database. The objective of this study was to record and evaluate trauma management at our university hospital and to measure the associated healthcare costs, while laying the foundations for a national database and the organization of regional trauma networks. Methods: Retrospective study of trauma patients (n=2320) between 2014 and 2015, through our single-center registry. Demographic information, injury patterns, hospital transfer, investigations, interventions, duration of hospitalization, Injury Severity Score (ISS), outcomes, complications and cost were recorded. Results: Road traffic collisions (RTC) accounted for 23.2% of traumas. The proportion of patients who were transferred to the hospital by the National Emergency Medical Services decreased throughout the study (n Discussion: Our results suggest that RTCs pose a significant financial burden. The prehospital triage of trauma patients is ineffective. A reduction of costs could have been achieved if prehospital triage was more effective. Level of evidence: Level IV.
Language	English
Publishing date	2020-03-17
Publishing country	England
Document type	Journal Article
ISSN	2397-5776
ISSN (online)	2397-5776
DOI	10.1136/tsaco-2019-000401
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: In vivo Simulation-Based Learning for Undergraduate Medical Students: Teaching and Assessment [Response to Letter].

Sideris, Michail / Hanrahan, John Gerrard / Nicolaides, Marios / Jagiello, Jade / Rallis, Kathrine S / Emin, Elif / Theodorou, Efthymia / Mallick, Rebecca / Odejinmi, Funlayo / Lymperopoulos, Nikolaos / Papalois, Apostolos / Tsoulfas, George

Advances in medical education and practice

2021 Volume 12, Page(s) 1221–1222

Language	English
Publishing date	2021-10-15
Publishing country	New Zealand
Document type	Journal Article ; Comment
ZDB-ID	2578539-4
ISSN	1179-7258
ISSN	1179-7258
DOI	10.2147/AMEP.S342865
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Streamlining Tangible 3D Printed and Intangible XR Content Creation and Evaluation: The ENTICE Experience.

Antoniou, Panagiotis / Sidiropoulos, Efstathios / Stathakarou, Natalia / Chatzimallis, Charalampos / Chondrokostas, Evangelos / Sumunar, Dimas / Karlsson, Tobias / Lachanoudi, Sofia / David, Panagiotis / Tagaras, Konstantinos / Varella, Annita / Athanasiou, Alkinoos / Pickering, James / Tooulias, Andreas / Karolos, Ion / Filippidis, Panagiotis-Marios / Schiza, Eirini / Voulgarakis, Vassilis / Bratsas, Charalambos /

Tsioukas, Vassilis / Tsoulfas, George / Bamidis, Panagiotis

Studies in health technology and informatics

2023 Volume 302, Page(s) 433–437

Abstract: ENTICE aimed to use co-creative methodologies in order to build a solid creation pipeline for medical experiential content. The project has developed and evaluated immersive learning resources and tools aiming to support well-defined learning objectives ... ...

Abstract	ENTICE aimed to use co-creative methodologies in order to build a solid creation pipeline for medical experiential content. The project has developed and evaluated immersive learning resources and tools aiming to support well-defined learning objectives using tangible and intangible resources (AR/VR/MR, 3D printing) that are highly sought in the fields of anatomy and surgery. In this paper the preliminary results from the evaluation of the learning resources and tools in 3 countries as well as the lessons learnt are presented towards to the improvement of the medical education process.
MeSH term(s)	Virtual Reality ; Learning ; Education, Medical ; Behavior Therapy ; Printing, Three-Dimensional
Language	English
Publishing date	2023-05-19
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI230167
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Article: In vivo Simulation-Based Learning for Undergraduate Medical Students: Teaching and Assessment.

Sideris, Michail / Nicolaides, Marios / Jagiello, Jade / Rallis, Kathrine S / Emin, Elif / Theodorou, Efthymia / Hanrahan, John Gerrard / Mallick, Rebecca / Odejinmi, Funlayo / Lymperopoulos, Nikolaos / Papalois, Apostolos / Tsoulfas, George

Advances in medical education and practice

2021 Volume 12, Page(s) 995–1002

Abstract: An increasing emphasis on simulation has become evident in the last three decades following fundamental shifts in the medical profession. Simulation-based learning (SBL) is a wide term that encompasses several means for imitating a skill, attitude, or ... ...

Abstract	An increasing emphasis on simulation has become evident in the last three decades following fundamental shifts in the medical profession. Simulation-based learning (SBL) is a wide term that encompasses several means for imitating a skill, attitude, or procedure to train personnel in a safe and adaptive environment. A classic example has been the use of live animal tissue, named in vivo SBL. We aimed to review all published evidence on in vivo SBL for undergraduate medical students; this includes both teaching concepts as well as focused assessment of students on those concepts. We performed a systematic review of published evidence on MEDLINE. We also incorporated evidence from a series of systematic reviews (eviCORE) focused on undergraduate education which have been outputs from our dedicated research network (eMERG). In vivo SBL has been shown to be valuable at undergraduate level and should be considered as a potential educational tool. Strict adherence to 3R (Reduce, Refine, Replace) principles in order to reduce animal tissue usage, should always be the basis of any curriculum. In vivo SBL could potentially grant an extra mile towards medical students' inspiration and aspiration to become safe surgeons; however, it should be optimised and supported by a well-designed curriculum which enhances learning via multi-level fidelity SBL.
Language	English
Publishing date	2021-08-30
Publishing country	New Zealand
Document type	Journal Article ; Review
ZDB-ID	2578539-4
ISSN	1179-7258
ISSN	1179-7258
DOI	10.2147/AMEP.S272185
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Article ; Online: Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

Al-Maini, Mustafa / Maindarkar, Mahesh / Kitas, George D / Khanna, Narendra N / Misra, Durga Prasanna / Johri, Amer M / Mantella, Laura / Agarwal, Vikas / Sharma, Aman / Singh, Inder M / Tsoulfas, George / Laird, John R / Faa, Gavino / Teji, Jagjit / Turk, Monika / Viskovic, Klaudija / Ruzsa, Zoltan / Mavrogeni, Sophie / Rathore, Vijay /

Miner, Martin / Kalra, Manudeep K / Isenovic, Esma R / Saba, Luca / Fouda, Mostafa M / Suri, Jasjit S

Rheumatology international

2023 Volume 43, Issue 11, Page(s) 1965–1982

Abstract: The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional ... ...

Abstract	The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP
MeSH term(s)	Humans ; Artificial Intelligence ; Cardiovascular Diseases/diagnosis ; Cardiovascular Diseases/etiology ; Cardiovascular Diseases/prevention & control ; Precision Medicine ; Arthritis, Rheumatoid/complications ; Stroke/etiology ; Stroke/prevention & control ; Myocardial Infarction ; Risk Assessment
Language	English
Publishing date	2023-08-30
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	8286-7
ISSN	1437-160X ; 0172-8172
ISSN (online)	1437-160X
ISSN	0172-8172
DOI	10.1007/s00296-023-05415-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

Dubey, Arun Kumar / Chabert, Gian Luca / Carriero, Alessandro / Pasche, Alessio / Danna, Pietro S C / Agarwal, Sushant / Mohanty, Lopamudra / Nillmani / Sharma, Neeraj / Yadav, Sarita / Jain, Achin / Kumar, Ashish / Kalra, Mannudeep K / Sobel, David W / Laird, John R / Singh, Inder M / Singh, Narpinder / Tsoulfas, George / Fouda, Mostafa M /

Alizad, Azra / Kitas, George D / Khanna, Narendra N / Viskovic, Klaudija / Kukuljan, Melita / Al-Maini, Mustafa / El-Baz, Ayman / Saba, Luca / Suri, Jasjit S

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 11

Abstract: Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo ... ...

Abstract	Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.
Language	English
Publishing date	2023-06-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13111954
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Cardiovascular disease/stroke risk stratification in deep learning framework: a review.

Bhagawati, Mrinalini / Paul, Sudip / Agarwal, Sushant / Protogeron, Athanasios / Sfikakis, Petros P / Kitas, George D / Khanna, Narendra N / Ruzsa, Zoltan / Sharma, Aditya M / Tomazu, Omerzu / Turk, Monika / Faa, Gavino / Tsoulfas, George / Laird, John R / Rathore, Vijay / Johri, Amer M / Viskovic, Klaudija / Kalra, Manudeep / Balestrieri, Antonella /

Nicolaides, Andrew / Singh, Inder M / Chaturvedi, Seemant / Paraskevas, Kosmas I / Fouda, Mostafa M / Saba, Luca / Suri, Jasjit S

Cardiovascular diagnosis and therapy

2023 Volume 13, Issue 3, Page(s) 557–598

Abstract: The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk ... ...

Abstract	The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.
Language	English
Publishing date	2023-06-05
Publishing country	China
Document type	Journal Article ; Review
ZDB-ID	2685043-6
ISSN	2223-3660 ; 2223-3652
ISSN (online)	2223-3660
ISSN	2223-3652
DOI	10.21037/cdt-22-438
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Article ; Online: Three-dimensional printing as an educational tool in colorectal surgery.

Bangeas, Petros / Drevelegas, Kostnantinos / Agorastou, Christina / Tzounis, Lazaros / Chorti, Aggeliki / Paramythiotis, Daniel / Michalopoulos, Antonis / Tsoulfas, George / Papadopoulos, Vassileios N / Exadaktylos, Aristomenis / Suri, Jasjit S

Frontiers in bioscience (Elite edition)

2019 Volume 11, Issue 1, Page(s) 29–37

Abstract: 3D printing is a rapidly advancing technology which represents a significant technological achievement that could be useful in a variety of biomedical applications. In the field of surgery, 3D printing is envisioned as a significant step in the areas of ... ...

Abstract	3D printing is a rapidly advancing technology which represents a significant technological achievement that could be useful in a variety of biomedical applications. In the field of surgery, 3D printing is envisioned as a significant step in the areas of surgical planning, education and training. The 3D printed models are considered as high quality and efficient educational tools. In this paper A randomized controlled trial was performed to compare the educational role of 3D printed models with that of the conventional MRI films in the training of surgical residents. Statistical analysis revealed that Resident surgeons who studied only the anal fistula printed models, (Group B) achieved a higher overall score in the fistula assessment test (87,2 (82,6-91,6)) compared to resident surgeons (Group A) who studied only MRI images (74,85 (66,8-73,5)). 3D printing technology can lead to improvement in preoperative planning accuracy, followed by efficient optimization of the treatment strategy. It is believed that 3D printing technology could be used in the case of various other surgical applications, thus representing a novel tool for surgical education.
MeSH term(s)	Digestive System Surgical Procedures/education ; Humans ; Magnetic Resonance Imaging ; Models, Anatomic ; Printing, Three-Dimensional ; Rectal Diseases/pathology ; Rectal Diseases/surgery
Language	English
Publishing date	2019-01-01
Publishing country	Singapore
Document type	Journal Article ; Randomized Controlled Trial
ZDB-ID	2565080-4
ISSN	1945-0508 ; 1945-0494
ISSN (online)	1945-0508
ISSN	1945-0494
DOI	10.2741/E844
Database	MEDical Literature Analysis and Retrieval System OnLINE

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