Cargando…
The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review
(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myomet...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416838/ https://www.ncbi.nlm.nih.gov/pubmed/37568955 http://dx.doi.org/10.3390/diagnostics13152592 |
_version_ | 1785087872690290688 |
---|---|
author | Petrila, Octavia Stefan, Anca-Elena Gafitanu, Dumitru Scripcariu, Viorel Nistor, Ionut |
author_facet | Petrila, Octavia Stefan, Anca-Elena Gafitanu, Dumitru Scripcariu, Viorel Nistor, Ionut |
author_sort | Petrila, Octavia |
collection | PubMed |
description | (1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: “endometrial cancer artificial intelligence”, “endometrial cancer AI”, “endometrial cancer MRI artificial intelligence”, “endometrial cancer machine learning”, and “endometrial cancer machine learning MRI”. We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients. |
format | Online Article Text |
id | pubmed-10416838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104168382023-08-12 The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review Petrila, Octavia Stefan, Anca-Elena Gafitanu, Dumitru Scripcariu, Viorel Nistor, Ionut Diagnostics (Basel) Systematic Review (1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: “endometrial cancer artificial intelligence”, “endometrial cancer AI”, “endometrial cancer MRI artificial intelligence”, “endometrial cancer machine learning”, and “endometrial cancer machine learning MRI”. We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients. MDPI 2023-08-03 /pmc/articles/PMC10416838/ /pubmed/37568955 http://dx.doi.org/10.3390/diagnostics13152592 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Systematic Review Petrila, Octavia Stefan, Anca-Elena Gafitanu, Dumitru Scripcariu, Viorel Nistor, Ionut The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review |
title | The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review |
title_full | The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review |
title_fullStr | The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review |
title_full_unstemmed | The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review |
title_short | The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review |
title_sort | applicability of artificial intelligence in predicting the depth of myometrial invasion on mri studies—a systematic review |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416838/ https://www.ncbi.nlm.nih.gov/pubmed/37568955 http://dx.doi.org/10.3390/diagnostics13152592 |
work_keys_str_mv | AT petrilaoctavia theapplicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT stefanancaelena theapplicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT gafitanudumitru theapplicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT scripcariuviorel theapplicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT nistorionut theapplicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT petrilaoctavia applicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT stefanancaelena applicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT gafitanudumitru applicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT scripcariuviorel applicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview AT nistorionut applicabilityofartificialintelligenceinpredictingthedepthofmyometrialinvasiononmristudiesasystematicreview |