{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:03:57Z","timestamp":1760598237327,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Houston Methodist Research Institute Clinician-Scholar program","award":["15790002"],"award-info":[{"award-number":["15790002"]}]},{"name":"Jerold B. Katz Academy of Translational Science","award":["15790002"],"award-info":[{"award-number":["15790002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The novel approach of our study consists in adapting and in evaluating a custom-made variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks (CNNs) on magnetic resonance imaging (MRI) images for differentiate soft vs. hard plaque components in peripheral arterial disease (PAD). Five amputated lower extremities were imaged at a clinical ultra-high field 7 Tesla MRI. Ultrashort echo time (UTE), T1-weighted (T1w) and T2-weighted (T2w) datasets were acquired. Multiplanar reconstruction (MPR) images were obtained from one lesion per limb. Images were aligned to each other and pseudo-color red-green-blue images were created. Four areas in latent space were defined corresponding to the sorted images reconstructed by the VAE. Images were classified from their position in latent space and scored using tissue score (TS) as following: (1) lumen patent, TS:0; (2) partially patent, TS:1; (3) mostly occluded with soft tissue, TS:3; (4) mostly occluded with hard tissue, TS:5. Average and relative percentage of TS was calculated per lesion defined as the sum of the tissue score for each image divided by the total number of images. In total, 2390 MPR reconstructed images were included in the analysis. Relative percentage of average tissue score varied from only patent (lesion #1) to presence of all four classes. Lesions #2, #3 and #5 were classified to contain tissues except mostly occluded with hard tissue while lesion #4 contained all (ranges (I): 0.2\u2013100%, (II): 46.3\u201375.9%, (III): 18\u201333.5%, (IV): 20%). Training the VAE was successful as images with soft\/hard tissues in PAD lesions were satisfactory separated in latent space. Using VAE may assist in rapid classification of MRI histology images acquired in a clinical setup for facilitating endovascular procedures.<\/jats:p>","DOI":"10.3390\/diagnostics13111925","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T01:40:56Z","timestamp":1685583656000},"page":"1925","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3792-527X","authenticated-orcid":false,"given":"Judit","family":"Csore","sequence":"first","affiliation":[{"name":"DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA"},{"name":"Heart and Vascular Center, Semmelweis University, 68 V\u00e1rosmajor Street, 1122 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christof","family":"Karmonik","sequence":"additional","affiliation":[{"name":"MRI Core, Translational Imaging Center, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, 77030 TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kayla","family":"Wilhoit","sequence":"additional","affiliation":[{"name":"MRI Core, Translational Imaging Center, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, 77030 TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lily","family":"Buckner","sequence":"additional","affiliation":[{"name":"MRI Core, Translational Imaging Center, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, 77030 TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trisha L.","family":"Roy","sequence":"additional","affiliation":[{"name":"DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18S","DOI":"10.1016\/j.jvs.2010.01.074","article-title":"Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial: Analysis of amputation free and overall survival by treatment received","volume":"51","author":"Bradbury","year":"2010","journal-title":"J. Vasc. Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1056\/NEJMoa2207899","article-title":"Surgery or Endovascular Therapy for Chronic Limb-Threatening Ischemia","volume":"387","author":"Farber","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1016\/j.jvs.2017.09.034","article-title":"Magnetic resonance imaging characteristics of lesions relate to the difficulty of peripheral arterial endovascular procedures","volume":"67","author":"Roy","year":"2018","journal-title":"J. Vasc. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3S","DOI":"10.1016\/j.jvs.2019.02.016","article-title":"Global vascular guidelines on the management of chronic limb-threatening ischemia","volume":"69","author":"Conte","year":"2019","journal-title":"J. Vasc. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1097\/RLI.0b013e3181c8cefa","article-title":"Quantification of Calcifications in Endarterectomy Samples by Means of High-Resolution Ultra-Short Echo Time Imaging","volume":"45","author":"Sharma","year":"2010","journal-title":"Investig. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"359","DOI":"10.2463\/mrms.mp.2020-0009","article-title":"Three Dimensional Ultra-short Echo Time MRI Can Depict Cholesterol Components of Gallstones Bright","volume":"20","author":"Takahashi","year":"2021","journal-title":"Magn. Reson. Med. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1016\/j.acra.2012.07.008","article-title":"In Vitro MR Imaging of Renal Stones with an Ultra-short Echo Time Magnetic Resonance Imaging Sequence","volume":"19","author":"Yassin","year":"2012","journal-title":"Acad. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1097\/RLI.0000000000000547","article-title":"Ultrashort Time to Echo Magnetic Resonance Evaluation of Calcium Pyrophosphate Crystal Deposition in Human Menisci","volume":"54","author":"Finkenstaedt","year":"2019","journal-title":"Investig. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e3859","DOI":"10.1002\/nbm.3859","article-title":"Visualization of calcium phosphate cement in teeth by zero echo time 1H MRI at high field","volume":"31","author":"Dou","year":"2018","journal-title":"NMR Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1002\/nbm.3372","article-title":"Characterization of the ultrashort-TE (UTE) MR collagen signal","volume":"28","author":"Siu","year":"2015","journal-title":"NMR Biomed."},{"key":"ref_11","first-page":"35","article-title":"Puncturing Plaques","volume":"24","author":"Roy","year":"2017","journal-title":"J. Endovasc. Ther. Off. J. Int. Soc. Endovasc. Spec."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1016\/j.mri.2019.12.006","article-title":"Artificial intelligence in medical imaging","volume":"68","author":"Gore","year":"2020","journal-title":"Magn. Reson. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"672","DOI":"10.3748\/wjg.v25.i6.672","article-title":"Artificial intelligence in medical imaging of the liver","volume":"25","author":"Zhou","year":"2019","journal-title":"World J. Gastroenterol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108774","DOI":"10.1016\/j.ejrad.2019.108774","article-title":"Artificial intelligence applications for thoracic imaging","volume":"123","author":"Chassagnon","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.crad.2019.02.006","article-title":"Artificial intelligence in breast imaging","volume":"74","author":"Le","year":"2019","journal-title":"Clin. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.jmir.2019.09.005","article-title":"Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging","volume":"50","author":"Currie","year":"2019","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29S","DOI":"10.2967\/jnumed.118.220590","article-title":"Artificial Intelligence in Nuclear Medicine","volume":"60","author":"Nensa","year":"2019","journal-title":"J. Nucl. Med."},{"key":"ref_18","first-page":"489","article-title":"Artificial Intelligence in Medical Imaging","volume":"90","author":"Wagner","year":"2019","journal-title":"Radiol. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20210406","DOI":"10.1259\/bjr.20210406","article-title":"Artificial intelligence in medical imaging: Implications for patient radiation safety","volume":"94","author":"Seah","year":"2021","journal-title":"Br. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109717","DOI":"10.1016\/j.ejrad.2021.109717","article-title":"Artificial intelligence in ultrasound","volume":"139","author":"Shen","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"196","DOI":"10.24920\/003963","article-title":"Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers","volume":"36","author":"Chen","year":"2021","journal-title":"Chin. Med. Sci. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.cpet.2021.09.005","article-title":"Evidence-Based Artificial Intelligence in Medical Imaging","volume":"17","author":"Streiner","year":"2022","journal-title":"PET Clin."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Singh, A., and Ogunfunmi, T. (2021). An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications. Entropy, 24.","DOI":"10.3390\/e24010055"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/neco_a_01458","article-title":"Predictive Coding, Variational Autoencoders, and Biological Connections","volume":"34","author":"Marino","year":"2021","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/TNNLS.2021.3096457","article-title":"Lifelong Mixture of Variational Autoencoders","volume":"34","author":"Ye","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1038\/s42003-022-03579-3","article-title":"Variational autoencoders learn transferrable representations of metabolomics data","volume":"5","author":"Gomari","year":"2022","journal-title":"Commun. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2737","DOI":"10.1109\/JBHI.2021.3123839","article-title":"Medical Data Wrangling with Sequential Variational Autoencoders","volume":"26","author":"Barrejon","year":"2022","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"238101","DOI":"10.1103\/PhysRevLett.125.238101","article-title":"Generative Embeddings of Brain Collective Dynamics Using Variational Autoencoders","volume":"125","author":"Perl","year":"2020","journal-title":"Phys. Rev. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1109\/JBHI.2020.3027443","article-title":"Improving Deep Reinforcement Learning with Transitional Variational Autoencoders: A Healthcare Application","volume":"25","author":"Baucum","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s11548-018-1898-0","article-title":"Unsupervised pathology detection in medical images using conditional variational autoencoders","volume":"14","author":"Uzunova","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/TMI.2020.3025065","article-title":"Uncertainty Quantification in Deep MRI Reconstruction","volume":"40","author":"Edupuganti","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.3934\/mbe.2020207","article-title":"A tutorial review of mathematical techniques for quantifying tumor heterogeneity","volume":"17","author":"Everett","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ahmad, B., Sun, J., You, Q., Palade, V., and Mao, Z. (2022). Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks. Biomedicines, 10.","DOI":"10.3390\/biomedicines10020223"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104939","DOI":"10.1016\/j.compbiomed.2021.104939","article-title":"Quantification of anomalies in rats\u2019 spinal cords using autoencoders","volume":"138","author":"Tschuchnig","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101952","DOI":"10.1016\/j.media.2020.101952","article-title":"Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study","volume":"69","author":"Baur","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1007\/s10278-020-00413-2","article-title":"Unsupervised Deep Anomaly Detection in Chest Radiographs","volume":"34","author":"Nakao","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102475","DOI":"10.1016\/j.media.2022.102475","article-title":"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers","volume":"79","author":"Pinaya","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_38","first-page":"3630","article-title":"Fusing multimodal neuroimaging data with a variational autoencoder","volume":"2021","author":"Geenjaar","year":"2021","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Guleria, H.V., Luqmani, A.M., Kothari, H.D., Phukan, P., Patil, S., Pareek, P., Kotecha, K., Abraham, A., and Gabralla, L.A. (2023). Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20054244"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Balaji, K. (2023). Image Augmentation based on Variational Autoencoder for Breast Tumor Segmentation. Acad. Radiol., 15.","DOI":"10.1016\/j.acra.2022.12.035"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s00354-022-00194-y","article-title":"Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images","volume":"41","author":"Chatterjee","year":"2023","journal-title":"New Gener. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106883","DOI":"10.1016\/j.cmpb.2022.106883","article-title":"WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections","volume":"221","author":"Zhou","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106093","DOI":"10.1016\/j.compbiomed.2022.106093","article-title":"StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder","volume":"149","author":"Chatterjee","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TNNLS.2019.2900734","article-title":"Semisupervised Text Classification by Variational Autoencoder","volume":"31","author":"Xu","year":"2020","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3032","DOI":"10.1109\/TCYB.2020.3023019","article-title":"Discriminative Mixture Variational Autoencoder for Semisupervised Classification","volume":"52","author":"Chen","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4926","DOI":"10.1039\/D1AY01257F","article-title":"Performing sequential forward selection and variational autoencoder techniques in soil classification based on laser-induced breakdown spectroscopy","volume":"13","author":"Harefa","year":"2021","journal-title":"Anal. Methods"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.patrec.2021.08.018","article-title":"Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification","volume":"151","author":"Mansour","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zheng, K., Wu, C., and Yang, Y. (2019). Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network. Sensors, 19.","DOI":"10.3390\/s19112528"},{"key":"ref_49","first-page":"80","article-title":"Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders","volume":"23","author":"Way","year":"2018","journal-title":"Pac. Symp. Biocomput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D Slicer as an image computing platform for the Quantitative Imaging Network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1038\/nmeth.2089","article-title":"NIH Image to ImageJ: 25 Years of image analysis","volume":"9","author":"Schneider","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_52","first-page":"E212","article-title":"Peripheral arterial calcification: Prevalence, mechanism, detection, and clinical implications","volume":"83","author":"Zeller","year":"2014","journal-title":"Catheter. Cardiovasc. Interv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1007\/s00270-014-0904-3","article-title":"Calcium Burden Assessment and Impact on Drug-Eluting Balloons in Peripheral Arterial Disease","volume":"37","author":"Fanelli","year":"2014","journal-title":"Cardiovasc. Interv. Radiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1016\/j.jvs.2015.06.137","article-title":"Acoustic shadowing impairs accurate characterization of stenosis in carotid ultrasound examinations","volume":"62","author":"Mohebali","year":"2015","journal-title":"J. Vasc. Surg."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1177\/1526602815604465","article-title":"Burning Bridges: Mechanisms and Implications of Endovascular Failure in the Treatment of Peripheral Artery Disease","volume":"22","author":"Roy","year":"2015","journal-title":"J. Endovasc. Ther."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1177\/1358863X18759826","article-title":"MRI for peripheral artery disease: Introductory physics for vascular physicians","volume":"23","author":"Roy","year":"2018","journal-title":"Vasc. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1002\/mrm.25320","article-title":"Projection MR imaging of peripheral arterial calcifications: Projection MR Imaging of Peripheral Arterial Calcifications","volume":"73","author":"Edelman","year":"2015","journal-title":"Magn. Reson. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"P135","DOI":"10.1186\/1532-429X-14-S1-P135","article-title":"Classification of human coronary atherosclerotic plaques with T1, T2 and Ultrashort TE MRI","volume":"14","author":"Karolyi","year":"2012","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.21037\/atm-20-7676","article-title":"Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: A narrative review for stroke application","volume":"9","author":"Saba","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lanzafame, L.R.M., Bucolo, G.M., Muscogiuri, G., Sironi, S., Gaeta, M., Ascenti, G., Booz, C., Vogl, T.J., Blandino, A., and Mazziotti, S. (2023). Artificial Intelligence in Cardiovascular CT and MR Imaging. Life, 13.","DOI":"10.3390\/life13020507"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Azeez, M., Laivuori, M., Tolva, J., Linder, N., Lundin, J., Alb\u00e4ck, A., Venermo, M., M\u00e4yr\u00e4np\u00e4\u00e4, M.I., Lokki, M.L., and Lokki, A.I. (2022). High relative amount of nodular calcification in femoral plaques is associated with milder lower extremity arterial disease. BMC Cardiovasc. Disord., 22.","DOI":"10.1186\/s12872-022-02945-7"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/11\/1925\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:46:12Z","timestamp":1760125572000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/11\/1925"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,31]]},"references-count":61,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["diagnostics13111925"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13111925","relation":{},"ISSN":["2075-4418"],"issn-type":[{"type":"electronic","value":"2075-4418"}],"subject":[],"published":{"date-parts":[[2023,5,31]]}}}