Zobrazeno 1 - 10
of 291
pro vyhledávání: '"J. Apostolopoulos"'
Autor:
Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Dimitrios J. Apostolopoulos, Elpiniki Papageorgiou
Publikováno v:
Bioengineering, Vol 11, Iss 10, p 957 (2024)
Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Fores
Externí odkaz:
https://doaj.org/article/bf8ad99130dc4d82b6460dfe829f367a
Autor:
Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, Dimitris J. Apostolopoulos, Nikolaos Papandrianos, Elpiniki I. Papageorgiou
Publikováno v:
Big Data and Cognitive Computing, Vol 8, Iss 8, p 85 (2024)
This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detectio
Externí odkaz:
https://doaj.org/article/f092ac60836446f8b8a8d598491ec4d7
Autor:
Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, Dimitris J. Apostolopoulos, Nikolaos Papandrianos, Elpiniki I. Papageorgiou
Publikováno v:
Diseases, Vol 12, Iss 6, p 115 (2024)
The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules’ (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital o
Externí odkaz:
https://doaj.org/article/70438bec41ba4f29b58b4d3b3ec1b674
Autor:
Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Elpiniki I. Papageorgiou, Dimitris J. Apostolopoulos
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 4, Iss 4, Pp 814-826 (2022)
Background: Recent advances in Artificial Intelligence (AI) algorithms, and specifically Deep Learning (DL) methods, demonstrate substantial performance in detecting and classifying medical images. Recent clinical studies have reported novel optical
Externí odkaz:
https://doaj.org/article/8be41d6be997493cb2059bb2620fccee
Autor:
Dimitris J. Apostolopoulos, Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, Trifon Spyridonidis, George S. Panayiotakis
Publikováno v:
Algorithms, Vol 16, Iss 9, p 435 (2023)
The pre-operative localisation of abnormal parathyroid glands (PG) in parathyroid scintigraphy is essential for suggesting treatment and assisting surgery. Human experts examine the scintigraphic image outputs. An assisting diagnostic framework for l
Externí odkaz:
https://doaj.org/article/33628341c6654e90adfb87f6346c53c6
Autor:
Ioannis D. Apostolopoulos, Nikolaοs D. Papathanasiou, Nikolaos Papandrianos, Elpiniki Papageorgiou, Dimitris J. Apostolopoulos
Publikováno v:
Applied Sciences, Vol 13, Iss 15, p 8839 (2023)
Greece is among the European Union members topping the list of deaths related to coronary artery disease. Myocardial Perfusion Imaging (MPI) with Single-Photon Emission Computed Tomography (SPECT) is a non-invasive test used to detect abnormalities i
Externí odkaz:
https://doaj.org/article/60944aa2848a42c0abf53f9d802d8402
Autor:
Dimitris J. Apostolopoulos, Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, Trifon Spyridonidis, George S. Panayiotakis
Publikováno v:
Algorithms, Vol 15, Iss 12, p 455 (2022)
Parathyroid scintigraphy with 99mTc-sestamibi (MIBI) is an established technique for localising abnormal parathyroid glands (PGs). However, the identification and localisation of PGs require much attention from medical experts and are time-consuming.
Externí odkaz:
https://doaj.org/article/f725fbc066b5428baed7951751020011
Publikováno v:
Diseases, Vol 10, Iss 3, p 56 (2022)
Background: Parathyroid proliferative disorder encompasses a wide spectrum of diseases, including parathyroid adenoma (PTA), parathyroid hyperplasia, and parathyroid carcinoma. Imaging modalities that deliver their results preoperatively help in the
Externí odkaz:
https://doaj.org/article/209f64eb0ec741ed8f37e9ce3775b801
Autor:
Nikolaos I. Papandrianos, Anna Feleki, Serafeim Moustakidis, Elpiniki I. Papageorgiou, Ioannis D. Apostolopoulos, Dimitris J. Apostolopoulos
Publikováno v:
Applied Sciences, Vol 12, Iss 15, p 7592 (2022)
Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model
Externí odkaz:
https://doaj.org/article/b7a7285cd7d541f3b3c16c2e430c4614
Publikováno v:
Reports, Vol 5, Iss 2, p 20 (2022)
X-ray technology has been recently employed for the detection of the lethal human coronavirus disease 2019 (COVID-19) as a timely, cheap, and helpful ancillary method for diagnosis. The scientific community evaluated deep learning methods to aid in t
Externí odkaz:
https://doaj.org/article/67b11ddbcf8348e0835e3cf3dfb6a063