Future skills-AI competencies for radiologists : Fostering AI knowledge and skills in undergraduate medical education
Radiologie (Heidelb). 2023 Nov 23. doi: 10.1007/s00117-023-01237-1. Online ahead of print.NO ABSTRACTPMID:37994912 | DOI:10.1007/s00117-023-01237-1 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Matthias Carl Laupichler Anoshirwan Andrej Tavakoli Tobias Raupach Daniel Paech Source Type: research

AI in Nuclear Medicine - a review of the current situation
Nuklearmedizin. 2023 Dec;62(6):332-333. doi: 10.1055/a-2198-0614. Epub 2023 Nov 23.NO ABSTRACTPMID:37995705 | DOI:10.1055/a-2198-0614 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Isabelle Miederer Julian Manuel Michael Rogasch Thomas Wendler Source Type: research

Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions
Nuklearmedizin. 2023 Dec;62(6):334-342. doi: 10.1055/a-2198-0358. Epub 2023 Nov 23.ABSTRACTPositron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline ...
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Dirk Hellwig Nils Constantin Hellwig Steven Boehner Timo Fuchs Regina Fischer Daniel Schmidt Source Type: research

Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine
Nuklearmedizin. 2023 Dec;62(6):343-353. doi: 10.1055/a-2200-2145. Epub 2023 Nov 23.ABSTRACTVolumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging tha...
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Thomas Wendler Michael C Kreissl Benedikt Schemmer Julian Manuel Michael Rogasch Francesca De Benetti Source Type: research

Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)
CONCLUSION: Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.PMID:37995708 | PMC:PMC10667066 | DOI:10.1055/a-2198-0545 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Julian Manuel Michael Rogasch Kuangyu Shi David Kersting Robert Seifert Source Type: research

Future skills-AI competencies for radiologists : Fostering AI knowledge and skills in undergraduate medical education
Radiologie (Heidelb). 2023 Nov 23. doi: 10.1007/s00117-023-01237-1. Online ahead of print.NO ABSTRACTPMID:37994912 | DOI:10.1007/s00117-023-01237-1 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Matthias Carl Laupichler Anoshirwan Andrej Tavakoli Tobias Raupach Daniel Paech Source Type: research

AI in Nuclear Medicine - a review of the current situation
Nuklearmedizin. 2023 Dec;62(6):332-333. doi: 10.1055/a-2198-0614. Epub 2023 Nov 23.NO ABSTRACTPMID:37995705 | DOI:10.1055/a-2198-0614 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Isabelle Miederer Julian Manuel Michael Rogasch Thomas Wendler Source Type: research

Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions
Nuklearmedizin. 2023 Dec;62(6):334-342. doi: 10.1055/a-2198-0358. Epub 2023 Nov 23.ABSTRACTPositron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline ...
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Dirk Hellwig Nils Constantin Hellwig Steven Boehner Timo Fuchs Regina Fischer Daniel Schmidt Source Type: research

Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine
Nuklearmedizin. 2023 Dec;62(6):343-353. doi: 10.1055/a-2200-2145. Epub 2023 Nov 23.ABSTRACTVolumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging tha...
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Thomas Wendler Michael C Kreissl Benedikt Schemmer Julian Manuel Michael Rogasch Francesca De Benetti Source Type: research

Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)
CONCLUSION: Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.PMID:37995708 | DOI:10.1055/a-2198-0545 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Julian Manuel Michael Rogasch Kuangyu Shi David Kersting Robert Seifert Source Type: research

Future skills-AI competencies for radiologists : Fostering AI knowledge and skills in undergraduate medical education
Radiologie (Heidelb). 2023 Nov 23. doi: 10.1007/s00117-023-01237-1. Online ahead of print.NO ABSTRACTPMID:37994912 | DOI:10.1007/s00117-023-01237-1 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 23, 2023 Category: Radiology Authors: Matthias Carl Laupichler Anoshirwan Andrej Tavakoli Tobias Raupach Daniel Paech Source Type: research

Nonosseous bone seeking tracer focal uptake in the conventional scintigraphy
Nuklearmedizin. 2023 Nov 22. doi: 10.1055/a-2198-0684. Online ahead of print.NO ABSTRACTPMID:37992726 | DOI:10.1055/a-2198-0684 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 22, 2023 Category: Radiology Authors: Manfred Fischer Jan Schneider-Eicke Source Type: research

Kommentar zu „NACHHALTIGKEIT POLITIK – Mit Power-Cycling Strom sparen“
Rofo. 2023 Dec;195(12):1071-1072. doi: 10.1055/a-2158-1663. Epub 2023 Nov 17.NO ABSTRACTPMID:37977181 | DOI:10.1055/a-2158-1663 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 17, 2023 Category: Radiology Authors: Tobias Heye Source Type: research

Kommentar zu „NACHHALTIGKEIT POLITIK – Mit Power-Cycling Strom sparen“
Rofo. 2023 Dec;195(12):1071-1072. doi: 10.1055/a-2158-1663. Epub 2023 Nov 17.NO ABSTRACTPMID:37977181 | DOI:10.1055/a-2158-1663 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 17, 2023 Category: Radiology Authors: Tobias Heye Source Type: research

Kommentar zu „NACHHALTIGKEIT POLITIK – Mit Power-Cycling Strom sparen“
Rofo. 2023 Dec;195(12):1071-1072. doi: 10.1055/a-2158-1663. Epub 2023 Nov 17.NO ABSTRACTPMID:37977181 | DOI:10.1055/a-2158-1663 (Source: Nuklearmedizin)
Source: Nuklearmedizin - November 17, 2023 Category: Radiology Authors: Tobias Heye Source Type: research