{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T15:00:26Z","timestamp":1762182026835,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Chest, abdominal, and pelvic computed tomography (CT) with intravenous contrast is widely used for tumor staging, treatment planning, and therapy monitoring. The integration of artificial intelligence (AI) services is expected to improve diagnostic accuracy across multiple anatomical regions simultaneously. We aimed to evaluate the diagnostic accuracy of a multi-target AI service in detecting 16 pathological features on chest and abdominal CT images. Methods: We conducted a retrospective study using anonymized CT data from an open dataset. A total of 229 CT scans were independently interpreted by four radiologists with more than 5 years of experience and analyzed by the AI service. Sixteen pathological features were assessed. AI errors were classified as minor, intermediate, or clinically significant. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Across 229 CT scans, the AI service made 423 errors (11.5% of all evaluated features, n = 3664). False positives accounted for 262 cases (61.9%) and false negatives for 161 (38.1%). Most errors were minor (62.9%) or intermediate (31.7%), while clinically significant errors comprised only 5.4%. The overall AUC of the AI service was 0.88 (95% CI: 0.87\u20130.89), compared with 0.78\u20130.81 for radiologists. For clinically significant findings, the AI AUC was 0.90 (95% CI: 0.71\u20131.00). Diagnostic accuracy was unsatisfactory only for urolithiasis. Conclusions: The multi-target AI service demonstrated high diagnostic accuracy for chest and abdominal CT interpretation, with most errors being clinically negligible; performance was limited for urolithiasis.<\/jats:p>","DOI":"10.3390\/diagnostics15212778","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:55:22Z","timestamp":1762178122000},"page":"2778","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Accuracy of a Multi-Target Artificial Intelligence Service for the Simultaneous Assessment of 16 Pathological Features on Chest and Abdominal CT"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6716-5593","authenticated-orcid":false,"given":"Valentin A.","family":"Nechaev","sequence":"first","affiliation":[{"name":"S.S. Yudin City Clinical Hospital, Moscow Department of Health, Moscow 129090, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2970-2394","authenticated-orcid":false,"given":"Nataliya Y.","family":"Kashtanova","sequence":"additional","affiliation":[{"name":"S.S. Yudin City Clinical Hospital, Moscow Department of Health, Moscow 129090, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9787-3804","authenticated-orcid":false,"given":"Evgenii V.","family":"Kopeykin","sequence":"additional","affiliation":[{"name":"S.S. Yudin City Clinical Hospital, Moscow Department of Health, Moscow 129090, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9187-1390","authenticated-orcid":false,"given":"Umamat M.","family":"Magomedova","sequence":"additional","affiliation":[{"name":"S.S. Yudin City Clinical Hospital, Moscow Department of Health, Moscow 129090, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6946-0625","authenticated-orcid":false,"given":"Maria S.","family":"Gribkova","sequence":"additional","affiliation":[{"name":"Multidisciplinary Medical Center of the Bank of Russia, Moscow 117647, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0050-5510","authenticated-orcid":false,"given":"Anton V.","family":"Hardin","sequence":"additional","affiliation":[{"name":"S.S. Yudin City Clinical Hospital, Moscow Department of Health, Moscow 129090, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0015-7094","authenticated-orcid":false,"given":"Marina I.","family":"Sekacheva","sequence":"additional","affiliation":[{"name":"World-Class Reserch Centre \u201cDigital Biodesign and Personalized Healthcare\u201d, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4840-4106","authenticated-orcid":false,"given":"Varvara D.","family":"Sanikovich","sequence":"additional","affiliation":[{"name":"World-Class Reserch Centre \u201cDigital Biodesign and Personalized Healthcare\u201d, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0302-293X","authenticated-orcid":false,"given":"Valeria Y.","family":"Chernina","sequence":"additional","affiliation":[{"name":"Intelligent Radiology Assistance Laboratories LLC (IRA LABS), Moscow 143026, Russia"},{"name":"Northern State Medical University, Arkhangelsk 163000, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1816-1315","authenticated-orcid":false,"given":"Victor A.","family":"Gombolevskiy","sequence":"additional","affiliation":[{"name":"World-Class Reserch Centre \u201cDigital Biodesign and Personalized Healthcare\u201d, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia"},{"name":"Northern State Medical University, Arkhangelsk 163000, Russia"},{"name":"AIRI, Moscow 105064, Russia"},{"name":"Russian Medical Academy of Continuous Professional Education, Moscow 125993, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111798","DOI":"10.1016\/j.ejrad.2024.111798","article-title":"AI in Radiology: From Promise to Practice\u2014A Guide to Effective Integration","volume":"181","author":"Katal","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1007\/s11547-024-01880-1","article-title":"Clinical Impact of AI in Radiology Department Management: A Systematic Review","volume":"129","author":"Buijs","year":"2024","journal-title":"Radiol. Med."},{"key":"ref_3","first-page":"82","article-title":"Ovarian Cancer, Primary Peritoneal Cancer, and Fallopian Tube Cancer: Practical Recommendations of RUSSCO, Part 1.2","volume":"14","author":"Pokataev","year":"2024","journal-title":"Malig. Tumors"},{"key":"ref_4","first-page":"105","article-title":"Diagnostic and Economic Evaluation of a Comprehensive Artificial Intelligence Algorithm for Detecting Ten Pathological Findings on Chest Computed Tomography","volume":"4","author":"Chernina","year":"2023","journal-title":"Diagnostics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1067\/j.cpradiol.2017.12.010","article-title":"Common Resident Errors When Interpreting Computed Tomography of the Abdomen and Pelvis: A Review of Types, Pitfalls, and Strategies for Improvement","volume":"48","author":"Allen","year":"2019","journal-title":"Curr. Probl. Diagn. Radiol."},{"key":"ref_6","first-page":"14","article-title":"Risk Factors for Perception Errors among Radiologists in the Analysis of Imaging Studies","volume":"17","author":"Nechaev","year":"2024","journal-title":"Vestnik Surgu. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"h5527","DOI":"10.1136\/bmj.h5527","article-title":"STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies","volume":"351","author":"Bossuyt","year":"2015","journal-title":"BMJ"},{"key":"ref_9","unstructured":"de la Iglesia Vay\u00e1, M., Saborit-Torres, J.M., Serrano, J.A.M., Oliver-Garcia, E., Pertusa, A., Bustos, A., Cazorla, M., Galant, J., Barber, X., and Orozco-Beltr\u00e1n, D. (2021). BIMCV COVID-19: A Large Annotated Dataset of RX and CT Images from COVID-19 Patients. IEEE Dataport."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105","DOI":"10.17816\/DD60622","article-title":"Inter-Observer Variability Between Readers of CT Images: All for One and One for All","volume":"2","author":"Kulberg","year":"2021","journal-title":"Digit. Diagn."},{"key":"ref_11","unstructured":"Gombolevskiy, V.A., Masri, A.G., Kim, S.Y., and Morozov, S.P. (2017). Manual for Radiology Technicians on Performing CT Examination Protocols. Methodological Guidelines No. 17, Moscow Healthcare Department. (In Russian)."},{"key":"ref_12","unstructured":"Morozov, S.P., Vladzimirskiy, A.V., and Klyashtorny, V.G. (2019). Clinical Trials of Software Based on Intelligent Technologies (Radiology). Best Practices in Radiology and Instrumental Diagnostics, Issue 23, Moscow Healthcare Department. (In Russian)."},{"key":"ref_13","unstructured":"Vasiliev, Y.A. (2023). Computer Vision in Radiology: The First Stage of the Moscow Experiment, Izdatelskie Resheniya. [2nd rev. and exp. ed.]. (In Russian)."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the Areas under Two or More Correlated ROC Curves: A Nonparametric Approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"ref_15","unstructured":"RStudio Team (2025, September 16). RStudio (Posit). Available online: https:\/\/posit.co\/."},{"key":"ref_16","unstructured":"Gamer, M., Lemon, J., Fellows, I., and Singh, P. (2025, September 16). irr: Various Coefficients of Interrater Reliability and Agreement. R Package Version 0.84.1. Available online: https:\/\/CRAN.R-project.org\/package=irr."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., and M\u00fcller, M. (2011). pROC: An Open-Source Package to Analyze and Compare ROC Curves. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-77"},{"key":"ref_18","unstructured":"GraphPad Software (2025, September 16). GraphPad Prism, Version 10.2.2. Available online: https:\/\/www.graphpad.com\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chakraborty, D.P. (2017). Observer Performance Methods for Diagnostic Imaging: Foundations, Modeling, and Applications with R-Based Examples, CRC Press.","DOI":"10.1201\/9781351228190"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e231236","DOI":"10.1148\/radiol.231236","article-title":"Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion","volume":"308","author":"Plesner","year":"2023","journal-title":"Radiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e037928","DOI":"10.1136\/bmjopen-2020-037928","article-title":"Intravenous Contrast CT versus Native CT in Acute Abdomen with Impaired Renal Function (INCARO): Study Protocol","volume":"10","author":"Mentula","year":"2020","journal-title":"BMJ Open"},{"key":"ref_22","first-page":"22","article-title":"A Review of Meta-Analyses on the Application of AI in Radiology","volume":"28","author":"Vasilev","year":"2024","journal-title":"Med. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic Accuracy of Deep Learning in Medical Imaging: A Systematic Review and Meta-Analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e496","DOI":"10.1016\/S2589-7500(21)00106-0","article-title":"Effect of a Comprehensive Deep-Learning Model on Chest X-Ray Interpretation Accuracy: A Retrospective MRMC Study","volume":"3","author":"Seah","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8263","DOI":"10.1007\/s00330-023-09747-1","article-title":"Can Incorrect AI Results Impact Radiologists? A Multi-Reader Pilot Study of Lung Cancer Detection with Chest Radiography","volume":"33","author":"Bernstein","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10549-024-07353-3","article-title":"Frequency and Characteristics of Errors by Artificial Intelligence (AI) in Reading Screening Mammography: A Systematic Review","volume":"207","author":"Zeng","year":"2024","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2431493","DOI":"10.2214\/AJR.24.31493","article-title":"Pitfalls in Interpretive Applications of AI in Radiology","volume":"223","author":"Behzad","year":"2024","journal-title":"Am. J. Roentgenol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pedrosa, J., Aresta, G., Ferreira, C., Carvalho, C., Silva, J., Sousa, P., Ribeiro, L., Mendon\u00e7a, A.M., and Campilho, A. (2022). Assessing Clinical Applicability of COVID-19 Detection in Chest Radiography with Deep Learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-10568-3"},{"key":"ref_29","first-page":"407","article-title":"Limitations in the Application of AI Services for Chest Radiograph Analysis","volume":"5","author":"Vasilev","year":"2024","journal-title":"Digit. Diagn."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/21\/2778\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T14:07:39Z","timestamp":1762178859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/21\/2778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":29,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["diagnostics15212778"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15212778","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]}}}