Artificial intelligence in fracture detection on radiographs: a literature review DOI
Antonio Lo Mastro,

Enrico Grassi,

Daniela Berritto

et al.

Japanese Journal of Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 14, 2024

Language: Английский

radMLBench: A dataset collection for benchmarking in radiomics DOI Creative Commons
Aydın Demircioğlu

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 182, P. 109140 - 109140

Published: Sept. 12, 2024

Language: Английский

Citations

2
The continuous improvement of digital assistance in the radiation oncologist’s work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO) DOI
Antonio Piras, I. Morelli, Riccardo Ray Colciago

et al.

La radiologia medica, Journal Year: 2024, Volume and Issue: 129(11), P. 1720 - 1735

Published: Oct. 13, 2024

Language: Английский

Citations

1
CLEAR guideline for radiomics: Early insights into current reporting practices endorsed by EuSoMII DOI
Burak Koçak, Andrea Ponsiglione, Arnaldo Stanzione

et al.

European Journal of Radiology, Journal Year: 2024, Volume and Issue: 181, P. 111788 - 111788

Published: Oct. 14, 2024

Language: Английский

Citations

1
Artificial intelligence in fracture detection on radiographs: a literature review DOI
Antonio Lo Mastro,

Enrico Grassi,

Daniela Berritto

et al.

Japanese Journal of Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 14, 2024

Language: Английский

Citations

0