Resolution Resampling of Ultrasound Images in Placenta Previa Patients: Influence on Radiomics Data Reliability and Usefulness for Machine Learning

[1]  Ron Kikinis,et al.  Repeatability of Multiparametric Prostate MRI Radiomics Features , 2018, Scientific Reports.

[2]  Tai-Lang Jong,et al.  Evaluation of placental maturity by the sonographic textures , 2011, Archives of Gynecology and Obstetrics.

[3]  Geoffrey G. Zhang,et al.  Voxel size and gray level normalization of CT radiomic features in lung cancer , 2018, Scientific Reports.

[4]  L. Cavallo,et al.  Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning , 2019, Neuroradiology.

[5]  Luigi Iuppariello,et al.  Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center , 2020, Comput. Methods Programs Biomed..

[6]  T. Todros,et al.  Ultrasound accuracy in prenatal diagnosis of abnormal placentation of posterior placenta previa. , 2019, European journal of obstetrics, gynecology, and reproductive biology.

[7]  A. Brunetti,et al.  Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa. , 2019, Magnetic resonance imaging.

[8]  L. Pace,et al.  Clinically significant prostate cancer detection on MRI: A radiomic shape features study. , 2019, European Journal of Radiology.

[9]  M. Cesarelli,et al.  Linear discriminant analysis and principal component analysis to predict coronary artery disease , 2020, Health Informatics J..

[10]  Maria Romano,et al.  Symbolic dynamic and frequency analysis in foetal monitoring , 2014, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[11]  R. Silver,et al.  Placenta accreta spectrum: accreta, increta, and percreta. , 2015, Obstetrics and gynecology clinics of North America.

[12]  R. Silver,et al.  Placenta Accreta Spectrum , 2018, The New England journal of medicine.

[13]  Gianni D'Addio,et al.  Machine learning can detect the presence of Mild cognitive impairment in patients affected by Parkinson’s Disease , 2020, 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[14]  Manjiri Dighe,et al.  Placental Imaging: Normal Appearance with Review of Pathologic Findings. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[15]  Sally Collins,et al.  Placenta accreta spectrum: pathophysiology and evidence‐based anatomy for prenatal ultrasound imaging , 2018, American journal of obstetrics and gynecology.

[16]  K. Borgwardt,et al.  Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.

[17]  Sally Collins,et al.  FIGO consensus guidelines on placenta accreta spectrum disorders: Prenatal diagnosis and screening , , 2018, International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics.

[18]  Paul Suetens,et al.  The Role of Medical Image Computing and Machine Learning in Healthcare , 2019, Artificial Intelligence in Medical Imaging.

[19]  Gianni D'Addio,et al.  Classifying patients affected by Parkinson’s disease into freezers or non-freezers through machine learning , 2020, 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[20]  C Ricciardi,et al.  Classifying the type of delivery from cardiotocographic signals: A machine learning approach , 2020, Comput. Methods Programs Biomed..

[21]  Paolo Bifulco,et al.  Feasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus , 2019, IFMBE Proceedings.

[22]  Maria Romano,et al.  Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography , 2019, IFMBE Proceedings.

[23]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[24]  Arturo Brunetti,et al.  Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results. , 2019, Academic radiology.

[25]  D. Carusi The Placenta Accreta Spectrum: Epidemiology and Risk Factors , 2018, Clinical obstetrics and gynecology.

[26]  Krzysztof J. Geras,et al.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. , 2019, Radiology.

[27]  Improta Giovanni,et al.  Distinguishing Functional from Non-functional Pituitary Macroadenomas with a Machine Learning Analysis , 2019, IFMBE Proceedings.

[28]  Francesco Amato,et al.  Evaluation of floatingline and foetal heart rate variability , 2018, Biomed. Signal Process. Control..

[29]  Bino A Varghese,et al.  Reliability of CT‐based texture features: Phantom study , 2019, Journal of applied clinical medical physics.

[30]  Jeffrey Dean,et al.  Machine Learning in Medicine , 2019, The New England journal of medicine.

[31]  Arturo Brunetti,et al.  Machine learning applications in prostate cancer magnetic resonance imaging , 2019, European Radiology Experimental.

[32]  A. Brunetti,et al.  Diagnostic accuracy of magnetic resonance imaging in assessing placental adhesion disorder in patients with placenta previa: Correlation with histological findings. , 2018, European journal of radiology.

[33]  D. Jurkovic,et al.  Placenta accreta: pathogenesis of a 20th century iatrogenic uterine disease. , 2012, Placenta.

[34]  A. Brunetti,et al.  US and MR imaging findings to detect placental adhesion spectrum (PAS) in patients with placenta previa: a comparative systematic study , 2019, Abdominal Radiology.

[35]  Thomas Booth,et al.  Machine learning and glioma imaging biomarkers , 2019, Clinical radiology.

[36]  S. Staibano,et al.  Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach , 2019, AntiCancer Research.