Current Applications of Artificial Intelligence in Sarcoidosis

Sève P et al (2021) Sarcoidosis: a clinical overview from symptoms to diagnosis. Cells 10(4):766. https://doi.org/10.3390/cells10040766

Article  CAS  PubMed  PubMed Central  Google Scholar 

Spagnolo P, Rossi G, Trisolini R, Sverzellati N, Baughman RP, Wells AU (2018) Pulmonary sarcoidosis. Lancet Respir Med 6(5):389–402. https://doi.org/10.1016/S2213-2600(18)30064-X

Article  PubMed  Google Scholar 

Nardi A et al (2011) Stage IV sarcoidosis: comparison of survival with the general population and causes of death. Eur Respir J 38(6):1368–1373. https://doi.org/10.1183/09031936.00187410

Article  CAS  PubMed  Google Scholar 

Mutasa S, Sun S, Ha R (2020) Understanding artificial intelligence based radiology studies: what is overfitting? Clin Imaging 65:96–99. https://doi.org/10.1016/j.clinimag.2020.04.025

Article  PubMed  PubMed Central  Google Scholar 

Wu Y, Wang H, Wu F (2017) Automatic classification of pulmonary tuberculosis and sarcoidosis based on random forest. In: 2017 10th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–5. https://doi.org/10.1109/CISP-BMEI.2017.8302280

de Lima AD, Lopes AJ, do Amaral JLM, de Melo PL (2022) Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis. BMC Med Inform Decis Mak 22(1):274. https://doi.org/10.1186/s12911-022-02021-2

Article  PubMed  PubMed Central  Google Scholar 

Bade G, Akhtar N, Trivedi A, Madan K, Guleria R, Talwar A (2021) Impulse oscillometry as a measure of airway dysfunction in sarcoidosis. Sarcoidosis Vasc Diffuse Lung Dis 38(3):e2021037. https://doi.org/10.36141/svdld.v38i3.8674

Article  PubMed  PubMed Central  Google Scholar 

“Imaging of sarcoidosis of the airways and lung parenchyma and correlation with lung function|European Respiratory Society.” https://erj-ersjournals-com.eresources.mssm.edu/content/40/3/750. Accessed Mar 16 2023

Mathai SV, Patel S, Jorde UP, Rochlani Y (2022) Epidemiology pathogenesis, and diagnosis of cardiac sarcoidosis. Methodist DeBakey Cardiovasc J 18(2):78–93. https://doi.org/10.14797/mdcvj.1057

Article  PubMed  PubMed Central  Google Scholar 

Ekström K et al (2019) Sudden death in cardiac sarcoidosis: an analysis of nationwide clinical and cause-of-death registries. Eur Heart J 40(37):3121–3128. https://doi.org/10.1093/eurheartj/ehz428

Article  PubMed  Google Scholar 

Dai Q, Sherif AA, Jin C, Chen Y, Cai P, Li P (2022) Machine learning predicting mortality in sarcoidosis patients admitted for acute heart failure. Cardiovasc Digit Health J 3(6):297–304. https://doi.org/10.1016/j.cvdhj.2022.08.001

Article  PubMed  PubMed Central  Google Scholar 

Okada DR et al (2019) Regional abnormalities on cardiac magnetic resonance imaging and arrhythmic events in patients with cardiac sarcoidosis. J Cardiovasc Electrophysiol 30(10):1967–1976. https://doi.org/10.1111/jce.14082

Article  PubMed  Google Scholar 

Shade JK et al (2021) Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier. Sci Adv. https://doi.org/10.1126/sciadv.abi8020

Article  PubMed  PubMed Central  Google Scholar 

Standardization of Uveitis Nomenclature (SUN) Working Group (2021) Classification criteria for sarcoidosis-associated uveitis. Am J Ophthalmol 228:220–230. https://doi.org/10.1016/j.ajo.2021.03.047

Article  Google Scholar 

Yoo S-J et al (2021) Automated lung segmentation on chest computed tomography images with extensive lung parenchymal abnormalities using a deep neural network. Korean J Radiol 22(3):476. https://doi.org/10.3348/kjr.2020.0318

Article  PubMed  Google Scholar 

Chen A, Karwoski RA, Gierada DS, Bartholmai BJ, Koo CW (2020) Quantitative CT analysis of diffuse lung disease. Radiographics 40(1):28–43. https://doi.org/10.1148/rg.2020190099

Article  PubMed  Google Scholar 

Newman F, Rosskamm S (2009) SU-FF-I-01: automated differential diagnoses of CT images from healthy lung and three pulmonary diseases using a simple statistical transform and a probabilistic neural network. Med Phys 36:2434–2434. https://doi.org/10.1118/1.3181120

Article  Google Scholar 

Baghdadi N, Maklad AS, Malki A, Deif MA (2022) Reliable sarcoidosis detection using chest X-rays with efficientnets and stain-normalization techniques. Sensors 22(10):3846. https://doi.org/10.3390/s22103846

Article  CAS  PubMed  PubMed Central  Google Scholar 

Togo R et al (2019) Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps. Comput Biol Med 104:81–86. https://doi.org/10.1016/j.compbiomed.2018.11.008

Article  PubMed  Google Scholar 

Katsushika S et al (2021) Deep learning algorithm to detect cardiac sarcoidosis from echocardiographic movies. Circ J Off J Jpn Circ Soc 86(1):87–95. https://doi.org/10.1253/circj.CJ-21-0265

Article  Google Scholar 

Muppidi A, Radfar M (2020) Löfgren’s syndrome sarcoidosis and Non-LS sarcoidosis prediction using 1d-convolutional neural networks. Inform Med Unlocked 19:100328. https://doi.org/10.1016/j.imu.2020.100328

Article  Google Scholar 

Lu C et al (2022) Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling. Int J Cardiovasc Imaging 38(8):1825–1836. https://doi.org/10.1007/s10554-022-02564-5

Article  PubMed  Google Scholar 

Barnes H et al (2022) Machine learning in radiology: the new frontier in interstitial lung diseases. Lancet Digit Health. https://doi.org/10.1016/S2589-7500(22)00230-8

Article  PubMed  Google Scholar 

Yu Y et al (2020) Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer. JAMA Netw Open 3(12):e2028086. https://doi.org/10.1001/jamanetworkopen.2020.28086

Article  PubMed  PubMed Central  Google Scholar 

Huang Y-Q et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol Off J Am Soc Clin Oncol 34(18):2157–2164. https://doi.org/10.1200/JCO.2015.65.9128

Article  Google Scholar 

Wang G et al (2021) Radiomics signature of brain metastasis: prediction of EGFR mutation status. Eur Radiol 31(7):4538–4547. https://doi.org/10.1007/s00330-020-07614-x

Article  CAS  PubMed  Google Scholar 

Ryan SM, Fingerlin T, Hamzeh N, Maier L, Carlson N (2018) An exploration of spatial radiomic features in pulmonary sarcoidosis. https://doi.org/10.48550/arXiv.1806.10281.

Culver DA, Baughman RP (2018) It’s time to evolve from Scadding: phenotyping sarcoidosis. Eur Respir J. https://doi.org/10.1183/13993003.00050-2018

Article  PubMed  Google Scholar 

Ryan SM et al (2019) Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis. Eur Respir J. https://doi.org/10.1183/13993003.00371-2019

Article  PubMed  PubMed Central  Google Scholar 

Nunes H, Soler P, Valeyre D (2005) Pulmonary sarcoidosis. Allergy 60(5):565–582. https://doi.org/10.1111/j.1398-9995.2005.00778.x

Article  CAS  PubMed  Google Scholar 

Lovinfosse P et al (2022) Distinction of lymphoma from sarcoidosis on 18F-FDG PET/CT: evaluation of radiomics-feature–guided machine learning versus human reader performance. J Nucl Med 63(12):1933–1940. https://doi.org/10.2967/jnumed.121.263598

Article  CAS  PubMed  PubMed Central  Google Scholar 

Obert M (2019) Are estimations of radiomic image markers dispensable due to recent deep learning findings? Eur Respir J. https://doi.org/10.1183/13993003.01185-2019

Article  PubMed  Google Scholar 

Newman LS et al (2004) A case control etiologic study of sarcoidosis: environmental and occupational risk factors. Am J Respir Crit Care Med 170(12):1324–1330. https://doi.org/10.1164/rccm.200402-249OC

Article  PubMed  Google Scholar 

Vagts C et al (2021) Unsupervised clustering reveals sarcoidosis phenotypes marked by a reduction in lymphocytes relate to increased inflammatory activity on 18FDG-PET/CT. Front Med. https://doi.org/10.3389/fmed.2021.595077

Article  Google Scholar 

Prasse A, Katic C, Germann M, Buchwald A, Zissel G, Müller-Quernheim J (2008) Phenotyping sarcoidosis from a pulmonary perspective. Am J Respir Crit Care Med 177(3):330–336. https://doi.org/10.1164/rccm.200705-742OC

Article  CAS  PubMed  Google Scholar 

Rodrigues SCS et al (2011) Factor analysis of sarcoidosis phenotypes at two referral centers in Brazil. Sarcoidosis Vasc Diffuse Lung Dis Off J WASOG 28(1):34–43

CAS  Google Scholar 

Schupp JC et al (2018) Phenotypes of organ involvement in sarcoidosis. Eur Respir J. https://doi.org/10.1183/13993003.00991-2017

Article  PubMed  Google Scholar 

He T et al (2023) Trends and opportunities in computable clinical phenotyping: a scoping review. J Biomed Inform 140:104335. https://doi.org/10.1016/j.jbi.2023.104335

Article  PubMed  Google Scholar 

Demanse D et al (2023) Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database. Semin Arthritis Rheum. 58:152140. https://doi.org/10.1016/j.semarthrit.2022.152140

Article  CAS  PubMed 

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