ITO: Conceptualization, Investigation, Visualization, Writing—original draft, Writing—review & editing, Validation. RMA and VM: Writing—review & editing. CS: Funding acquisition, Writing—original draft, Writing—review & editing. SRB: Supervision, Writing—original draft, Writing—review & editing. All authors read and approved the submitted version.
Conflicts of interest
The authors declare that they have no conflicts of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent to publication
Not applicable.
Availability of data and materials
Not applicable.
Funding
This study was supported by the German Federal Ministry of Education [Clusters4Future SaxoCell, 03ZU1111DA] and by the German Research Foundation DFG, project [314061271] and project [288034826]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Oikonomakos IT, Steenblock C, Bornstein SR. Artificial intelligence in diabetes mellitus and endocrine diseases—What can we expect?Nat Rev Endocrinol. 2023;19:375–6. [DOI]
Gardiner LJ, Rusholme-Pilcher R, Colmer J, Rees H, Crescente JM, Carrieri AP, et al. Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.Proc Natl Acad Sci U S A. 2021;118:e2103070118. [DOI] [PubMed] [PMC]
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database.NPJ Digit Med. 2020;3:118. [DOI] [PubMed] [PMC]
Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis.Lancet Digit Health. 2021;3:e195–203. [DOI] [PubMed]
da Rocha Fernandes J, Ogurtsova K, Linnenkamp U, Guariguata L, Seuring T, Zhang P, et al. IDF diabetes atlas estimates of 2014 global health expenditures on diabetes.Diabetes Res Clin Pract. 2016;117:48–54. [DOI] [PubMed]
Colagiuri S, Davies D. The value of early detection of type 2 diabetes.Curr Opin Endocrinol Diabetes Obes. 2009;16:95–9. [DOI] [PubMed]
Herman WH, Ye W, Griffin SJ, Simmons RK, Davies MJ, Khunti K, et al. Early detection and treatment of type 2 diabetes reduce cardiovascular morbidity and mortality: a simulation of the results of the anglo-danish-dutch study of intensive treatment in people with screen-detected diabetes in primary care (ADDITION-Europe).Diabetes Care. 2015;38:1449–55. [DOI] [PubMed] [PMC]
Perveen S, Shahbaz M, Keshavjee K, Guergachi A. Prognostic modeling and prevention of diabetes using machine learning technique.Sci Rep. 2019;9:13805. [DOI] [PubMed] [PMC]
Zhang L, Wang Y, Niu M, Wang C, Wang Z. Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the henan rural cohort study.Sci Rep. 2020;10:4406. [DOI] [PubMed] [PMC]
Ravaut M, Sadeghi H, Leung KK, Volkovs M, Kornas K, Harish V, et al. Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.NPJ Digit Med. 2021;4:24. [DOI] [PubMed] [PMC]
Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables.Lancet Diabetes Endocrinol. 2018;6:361–9. [DOI] [PubMed]
Anjana RM, Baskar V, Nair ATN, Jebarani S, Siddiqui MK, Pradeepa R, et al. Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: a data-driven cluster analysis: the INSPIRED study.BMJ Open Diabetes Res Care. 2020;8:e001506. [DOI] [PubMed] [PMC]
Du Y, Rafferty AR, McAuliffe FM, Wei L, Mooney C. An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus.Sci Rep. 2022;12:1170. [DOI] [PubMed] [PMC]
Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning.Ophthalmology. 2017;124:962–9. [DOI] [PubMed]
Pradeepa R, Rajalakshmi R, Mohan V. Use of telemedicine technologies in diabetes prevention and control in resource-constrained settings: lessons learned from emerging economies.Diabetes Technol Ther. 2019;21:S2–9–16. [DOI] [PubMed]
Al-hazaimeh OM, Abu-Ein A, Tahat N, Al-Smadi Mm, Al-Nawashi M. Combining artificial intelligence and image processing for diagnosing diabetic retinopathy in retinal fundus Iiages.iJOE. 2022;18:131–51. [DOI]
Huang X, Wang H, She C, Feng J, Liu X, Hu X, et al. Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.Front Endocrinol (Lausanne). 2022;13:946915. [DOI] [PubMed] [PMC]
Bryan JM, Bryar PJ, Mirza RG. Convolutional neural networks accurately identify ungradable images in a diabetic retinopathy telemedicine screening program.Telemed J E Health. 2023;29:1349–55. [DOI] [PubMed]
Bornstein SR, Ludwig B, Steenblock C. Progress in islet transplantation is more important than ever.Nat Rev Endocrinol. 2022;18:389–90. [DOI] [PubMed] [PMC]
Nimri R, Battelino T, Laffel LM, Slover RH, Schatz D, Weinzimer SA, et al.; NextDREAM Consortium. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes.Nat Med. 2020;26:1380–4. [DOI] [PubMed]
Yoo JH, Kim JH. Advances in continuous glucose monitoring and integrated devices for management of diabetes with insulin-based therapy: improvement in glycemic control.Diabetes Metab J. 2023;47:27–41. [DOI] [PubMed] [PMC]
Foersch S, Glasner C, Woerl AC, Eckstein M, Wagner DC, Schulz S, et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer.Nat Med. 2023;29:430–9. [DOI] [PubMed]
Liu H, Guan X, Xu B, Zeng F, Chen C, Yin HL, et al. Computed tomography-based machine learning differentiates adrenal pheochromocytoma from lipid-poor adenoma.Front Endocrinol (Lausanne). 2022;13:833413. [DOI] [PubMed] [PMC]
Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, et al. Swarm learning for decentralized artificial intelligence in cancer histopathology.Nat Med. 2022;28:1232–9. [DOI] [PubMed] [PMC]
Tessler FN, Thomas J. Artificial intelligence for evaluation of thyroid nodules: a primer.Thyroid. 2023;33:150–8. [DOI] [PubMed]
Wang Z, Qu L, Chen Q, Zhou Y, Duan H, Li B, et al. Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer.BMC Cancer. 2023;23:128. [DOI] [PubMed] [PMC]
Turai PI, Herold Z, Nyirő G, Borka K, Micsik T, Tőke J, et al. Tissue miRNA combinations for the differential diagnosis of adrenocortical carcinoma and adenoma established by artificial intelligence.Cancers (Basel). 2022;14:895. [DOI] [PubMed] [PMC]
Shi X, Liu Y, Cheng S, Hu H, Zhang J, Wei M, et al. Cancer stemness associated with prognosis and the efficacy of immunotherapy in adrenocortical carcinoma.Front Oncol. 2021;11:651622. [DOI] [PubMed] [PMC]
Wang X, Heinrich DA, Kunz SL, Heger N, Sturm L, Uhl O, et al. Characteristics of preoperative steroid profiles and glucose metabolism in patients with primary aldosteronism developing adrenal insufficiency after adrenalectomy.Sci Rep. 2021;11:11181. [DOI] [PubMed] [PMC]
Hellwege JN, Palmer ND, Raffield LM, Ng MC, Hawkins GA, Long J, et al. Genome-wide family-based linkage analysis of exome chip variants and cardiometabolic risk.Genet Epidemiol. 2014;38:345–52. [DOI] [PubMed] [PMC]
Lin E, Lane HY. Machine learning and systems genomics approaches for multi-omics data.Biomark Res. 2017;5:2. [DOI] [PubMed] [PMC]
Jung LC, Wang H, Li X, Wu C. A machine learning method for selection of genetic variants to increase prediction accuracy of type 2 diabetes mellitus using sequencing data.Stat Anal Data Min: The ASA Data Sci J. 2020;13:261–81. [DOI]
Hahn SJ, Kim S, Choi YS, Lee J, Kang J. Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: a machine learning analysis of population-based 10-year prospective cohort study.EBioMedicine. 2022;86:104383. [DOI] [PubMed] [PMC]
Hathaway QA, Roth SM, Pinti MV, Sprando DC, Kunovac A, Durr AJ, et al. Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.Cardiovasc Diabetol. 2019;18:78. [DOI] [PubMed] [PMC]
Barat M, Gaillard M, Cottereau AS, Fishman EK, Assié G, Jouinot A, et al. Artificial intelligence in adrenal imaging: a critical review of current applications.Diagn Interv Imaging. 2023;104:37–42. [DOI] [PubMed]
Eisenhofer G, Durán C, Cannistraci CV, Peitzsch M, Williams TA, Riester A, et al. Use of steroid profiling combined with machine learning for identification and subtype classification in primary aldosteronism.JAMA Netw Open. 2020;3:e2016209. [DOI] [PubMed] [PMC]
Reel S, Reel PS, Erlic Z, Amar L, Pecori A, Larsen CK, et al. Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios.Metabolites. 2022;12:755. [DOI] [PubMed] [PMC]
Lam AQ, Freedman BS, Morizane R, Lerou PH, Valerius MT, Bonventre JV. Rapid and efficient differentiation of human pluripotent stem cells into intermediate mesoderm that forms tubules expressing kidney proximal tubular markers.J Am Soc Nephrol. 2014;25:1211–25. [DOI] [PubMed] [PMC]
Takasato M, Er PX, Chiu HS, Maier B, Baillie GJ, Ferguson C, et al. Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis.Nature. 2015;526:564–8. [DOI] [PubMed]
Torres J, Prieto J, Durupt FC, Broad S, Watt FM. Efficient differentiation of embryonic stem cells into mesodermal precursors by BMP, retinoic acid and Notch signalling.PLoS One. 2012;7:e36405. [DOI] [PubMed] [PMC]
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): IEEE Computer Society; 2015 Jun 7–12. Boston, MA, USA: IEEE; 2015. pp. 1–9.
Shorten C, Khoshgoftaar ™. A survey on image data augmentation for deep learning.J Big Data. 2019;6:60. [DOI]
Gill ME, Quaas AM. Looking with new eyes: advanced microscopy and artificial intelligence in reproductive medicine.J Assist Reprod Genet. 2023;40:235–9. [DOI] [PubMed]
Medenica S, Zivanovic D, Batkoska L, Marinelli S, Basile G, Perino A, et al. The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes—the value of regulatory frameworks.Diagnostics (Basel). 2022;12:2979. [DOI] [PubMed] [PMC]
Dimai HP. New horizons: artificial intelligence tools for managing osteoporosis.J Clin Endocrinol Metab. 2023;108:775–83. [DOI] [PubMed] [PMC]
Fasihi L, Tartibian B, Eslami R, Fasihi H. Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols.Sci Rep. 2022;12:18330. [DOI] [PubMed] [PMC]
Kizilgul M, Karakis R, Dogan N, Bostan H, Yapici MM, Gul U, et al. Real-time detection of acromegaly from facial images with artificial intelligence.Eur J Endocrinol. 2023;188:lavd005. [DOI] [PubMed]
Pappachan JM, Cassidy B, Fernandez CJ, Chandrabalan V, Yap MH. The role of artificial intelligence technology in the care of diabetic foot ulcers: the past, the present, and the future.World J Diabetes. 2022;13:1131–9. [DOI] [PubMed] [PMC]
Choi B, Jang JH, Son M, Lee MS, Jo YY, Jeon JY, et al. Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism.Eur Heart J Digit Health. 2022;3:255–64. [DOI] [PubMed] [PMC]
Naser JA, Lopez-Jimenez F, Chang AY, Baez-Suarez A, Attia ZI, Pislaru SV, et al. Artificial intelligence-augmented electrocardiogram in determining sex: correlation with sex hormone levels.Mayo Clin Proc. 2023;98:541–8. [DOI] [PubMed]
Kopitar L, Kocbek P, Cilar L, Sheikh A, Stiglic G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models.Sci Rep. 2020;10:11981. [DOI] [PubMed] [PMC]
Pamporaki C, Berends AMA, Filippatos A, Prodanov T, Meuter L, Prejbisz A, et al. Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort.Lancet Digit Health. 2023;5:e551–9. [DOI] [PubMed] [PMC]
Thomasian NM, Kamel IR, Bai HX. Machine intelligence in non-invasive endocrine cancer diagnostics.Nat Rev Endocrinol. 2022;18:81–95. [DOI] [PubMed] [PMC]
Wallace PW, Conrad C, Brückmann S, Pang Y, Caleiras E, Murakami M, et al. Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.J Pathol. 2020;251:378–87. [DOI] [PubMed] [PMC]
Chen RJ, Lu MY, Williamson DFK, Chen TY, Lipkova J, Noor Z, et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning.Cancer Cell. 2022;40:865–78.e6. [DOI] [PubMed] [PMC]
Cheng CY, Li Y, Varala K, Bubert J, Huang J, Kim GJ, et al. Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.Nat Commun. 2021;12:5627. [DOI] [PubMed] [PMC]
Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges.N Engl J Med. 2018;378:981–3. [DOI] [PubMed] [PMC]
Petch J, Di S, Nelson W. Opening the black box: the promise and limitations of explainable machine learning in cardiology.Can J Cardiol. 2022;38:204–13. [DOI] [PubMed]
DeGrave AJ, Janizek JD, Lee SI. AI for radiographic COVID-19 detection selects shortcuts over signal.Nat Mac Intell. 2021;3:610–9. [DOI] [PubMed] [PMC]
Choi HJ, Wang C, Pan X, Jang J, Cao M, Brazzo JA 3rd, et al. Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.Phys Biol. 2021;18:041001. [DOI] [PubMed] [PMC]
Guo J, Wang P, Sozen B, Qiu H, Zhu Y, Zhang X, et al. Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro.Stem Cell Reports. 2021;16:1331–46. [DOI] [PubMed] [PMC]
