Ebbehoj A, Li D, Kaur RJ, Zhang C, Singh S, Li T, Atkinson E, Achenbach S, Khosla S, Arlt W, Young WF, Rocca WA, Bancos I (2020) Epidemiology of adrenal tumours in olmsted county, minnesota, USA: a population-based cohort study. Lancet Diabetes Endocrinol 8(11):894–902. https://doi.org/10.1016/S2213-8587(20)30314-4

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fassnacht M, Arlt W, Bancos I, Dralle H, Newell-Price J, Sahdev A, Tabarin A, Terzolo M, Tsagarakis S, Dekkers OM (2016) Management of adrenal incidentalomas: European society of endocrinology clinical practice guideline in collaboration with the European network for the study of adrenal tumors. Eur J Endocrinol 175(2):G1–G34. https://doi.org/10.1530/EJE-16-0467

Article  CAS  PubMed  Google Scholar 

Di Sarno L, Caroselli A, Tonin G, Graglia B, Pansini V, Causio FA, Gatto A, Chiaretti A (2024) Artificial intelligence in pediatric emergency medicine: applications, challenges, and future perspectives. Biomedicines 12:1220. https://doi.org/10.3390/biomedicines12061220

Article  PubMed  PubMed Central  Google Scholar 

Mennickent D, Romero-Albornoz L, Gutiérrez-Vega S, Aguayo C, Marini F, Guzmán-Gutiérrez E, Araya J (2024) Simple and fast prediction of gestational diabetes mellitus based on machine learning and Near-Infrared spectra of serum: A proof of concept study at different stages of pregnancy. Biomedicines 12:1142. https://doi.org/10.3390/biomedicines12061142

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kasap DNG, Mora NGN, Blömer DA, Akkurt BH, Heindel WL, Mannil M, Musigmann M (2024) Comparison of MRI sequences to predict IDH mutation status in gliomas using Radiomics-Based machine learning. Biomedicines 12:725. https://doi.org/10.3390/biomedicines12040725

Article  CAS  PubMed  PubMed Central  Google Scholar 

Piccirillo G, Moscucci F, Mezzadri M, Caltabiano C, Cisaria G, Vizza G, De Santis V, Giuffrè M, Stefano S, Scinicariello C et al (2024) Artificial intelligence applied to electrical and Non-Invasive hemodynamic markers in elderly decompensated chronic heart failure patients. Biomedicines 12:716. https://doi.org/10.3390/biomedicines12040716

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bonnell J, Alcazar O, Watts B, Buchwald P, Abdulreda MH, Ogihara M (2024) Supervised parametric learning in the identification of composite biomarker signatures of type 1 diabetes in integrated parallel Multi-Omics datasets. Biomedicines 12:492. https://doi.org/10.3390/biomedicines12030492

Article  PubMed  PubMed Central  Google Scholar 

Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F (2023) Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inf Assoc 30:2050–2063. https://doi.org/10.1093/jamia/ocad180

Article  Google Scholar 

Xiao D, Zhong J, Peng J, Fan C, Wang X, Wen X, Liao W, Wang J, Yin X (2023) Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics. BMC Med Imaging 23:159. https://doi.org/10.1186/s12880-023-01106-2

Article  PubMed  PubMed Central  Google Scholar 

Piskin FC, Akkus G, Yucel SP, Unal I, Balli HT, Olgun ME, Sert M, Tetiker BT, Aikimbaev K (2023) A machine learning approach to distinguishing between non-functioning and autonomous cortisol secreting adrenal incidentaloma on magnetic resonance imaging using texture analysis. Ir J Med Sci 192:1155–1161. https://doi.org/10.1007/s11845-022-03105-8

Article  PubMed  Google Scholar 

Romeo V, Maurea S, Cuocolo R, Petretta M, Mainenti PP, Verde F, Coppola M, Dell’Aversana S, Brunetti A (2018) Characterization of adrenal lesions on unenhanced MRI using texture analysis: A Machine-Learning approach. J Magn Reson Imaging 48:198–204. https://doi.org/10.1002/jmri.25954

Article  PubMed  Google Scholar 

Liu H, Guan X, Xu B, Zeng F, Chen C, Yin HL, Yi X, Peng Y, Chen BT (2022) Computed Tomography-Based machine learning differentiates adrenal pheochromocytoma from Lipid-Poor adenoma. Front Endocrinol (Lausanne) 21:833413. https://doi.org/10.3389/fendo.2022.833413

Article  Google Scholar 

Maggio R, Messina F, D’Arrigo B, Maccagno G, Lardo P, Palmisano C, Poggi M, Monti S, Matarazzo I, Laghi A, Pugliese G, Stigliano A (2022) Machine Learning-Based texture analysis in the characterization of cortisol secreting vs. Non-Secreting adrenocortical incidentalomas in CT scan. Front Endocrinol (Lausanne) 13:873189. https://doi.org/10.3389/fendo.2022.873189

Article  PubMed  Google Scholar 

Feliciani G, Serra F, Menghi E, Ferroni F, Sarnelli A, Feo C, Zatelli MC, Ambrosio MR, Giganti M, Carnevale A (2024) Radiomics in the characterization of lipid-poor adrenal adenomas at unenhanced CT: time to look beyond usual density metrics. Eur Radiol 422–432. https://doi.org/10.1007/s00330-023-10090-8

Oloukoi C, Dohan A, Gaillard M, Hoeffel C, Groussin-Rouiller L, Bertherat J, Jouinot A, Assié G, Fuks D, Sibony M, Soyer P, Jannot AS, Barat M (2024) Differentiation between adrenocortical carcinoma and lipid-poor adrenal adenoma using a multiparametric MRI-based diagnostic algorithm. Diagn Interv Imaging. https://doi.org/10.1016/j.diii.2024.03.005

Article  PubMed  Google Scholar 

Fassnacht M, Tsagarakis S, Terzolo M, Tabarin A, Sahdev, Newell-Price J, Pelsma I, Marina L, Lorenz K, Bancos I, Arlt W, Dekkers OM (2023) European society of endocrinology clinical practice guidelines on the management of adrenal incidentalomas, in collaboration with the European network for the study of adrenal tumors. Eur J Endocrinol 189(1):1–42. https://doi.org/10.1093/ejendo/lvad066

Article  CAS  Google Scholar 

Alwosheel A, van Cranenburgh S, Chorus CG (2018) Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J Choice Model 28:167–182. https://doi.org/10.1016/j.jocm.2018.07.002

Article  Google Scholar 

Hosmer DW, Lemeshow S (2024) Applied logistic regression. Wiley

Hastie T, Tibshirani R, Friedman J (2009) Basis expansions and regularization. The elements of statistical learning. Springer USA, New York, pp 139–189

Chapter  Google Scholar 

Alpaydin E (2014) Introduction to Machine Learning. 3rd ed. MIT Press: Cambridge, MA, 2014

Raschka S, Mirjalili V (2019) Python machine learning: machine learning and deep learning with python, Scikit-Learn and tensorflow 2, 3rd edn. Packt Publishing, Birmingham

Google Scholar 

Chakravarthy SRS, Rajaguru H (2020) Voting Based CAD Model for Breast Cancer Classification. In Proceedings of the 7th International Conference on Advancements of Medicine and Health Care through Technology, Cluj-Napoca, Romania

Rish I (2001) An empirical study of naive Bayes classifier variants for sentiment analysis. Journal of King Saud University - Computer and Information Sciences. In Proceedings of the JCAI 2001 Workshop on Empirical Methods in Artificial Intelligence 3:41–46

Velupillai S, Suominen H, Liakata M, Roberts A, Shah AD, Morley K, Osborn D, Hayes J, Stewart R, Downs J, Chapman W, Dutta R (2018) Using clinical natural Language processing for health outcomes research: overview and actionable suggestions for future advances. J Biomed Inf 88:11–19. https://doi.org/10.1016/j.jbi.2018.10.005

Article  Google Scholar 

Bottou L, Curtis FE, Nocedal J (2016) Optimization methods for Large-Scale machine learning. SIAM Rev 60

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118. https://doi.org/10.1038/nature21056

Article  CAS  PubMed  PubMed Central  Google Scholar 

Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: Bagging-, Boosting-, and Hybrid-Based approaches. IEEE Trans Syst Man Cybernetics Part C (Applications Reviews) 42:463–484

Article  Google Scholar 

Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H (2014) Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study. Am J Epidemiol 179:764–774. https://doi.org/10.1093/aje/kwt312

Article  PubMed  PubMed Central  Google Scholar 

Antoniadi AM, Du Y, Guendouz Y, Wei L, Mazo C, Becker BA, Mooney C (2021) Current challenges and future opportunities for XAI in machine Learning-Based clinical decision support systems: A systematic review. Appl Sci 11:5088. https://doi.org/10.3390/app11115088

Article  CAS  Google Scholar 

Grandini M, Bagli E, Visani G (2020) Metrics for multi-class classification: an overview. arXiv:200805756. Accessed 21 June 2024

Tongman S, Chanama S, Chanama M, Plaimas K, Lursinsap C (2017) Metabolic pathway synthesis based on predicting compound transformable pairs by using neural classifiers with imbalanced data handling. Expert Syst Appl 88:45–57. https://doi.org/10.1016/j.eswa.2017.06.026

Article  Google Scholar 

Dhilsath Fathima M, Hariharan R, Raja S (2023) Multiple imputation by chained equations-k-nearest neighbors and deep neural network architecture for kidney disease prediction. Int J Image Grap 23:2350014

Article  Google Scholar 

Python (2024) Available online: https://www.python.org/. Accessed 21 June

Giorgini F, Di Dalmazi G, Diciotti S (2024) Artificial intelligence in endocrinology: a comprehensive review. J Endocrinol Invest 47:1067–1082

Article  CAS  PubMed  Google Scholar 

Marquardt A, Landwehr L-S, Ronchi CL, Di Dalmazi G, Riester A, Kollmannsberger P et al (2021) Identifying new potential biomarkers in adrenocortical tumors based on mRNA expression data using machine learning. Cancers 13:4671. https://doi.org/10.3390/cancers13184671

Article  CAS  PubMed  PubMed Central  Google Scholar 

Olsen H, Olsen M (2023) Associations of age, BMI, and renal function to cortisol after dexamethasone suppression in patients with adrenal incidentalomas. Front Endocrinol (Lausanne) 13:1055298. https://doi.org/10.3389/fendo.2022.1055298

Article