{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T10:33:56Z","timestamp":1782556436224,"version":"3.54.5"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model\/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83\u201391%), specificity of 94% (92\u201396%), and AUC of 97% (95\u201398%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (\u2264 200 or\u2009&gt;\u2009\u00a0200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02397-9","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T13:02:27Z","timestamp":1704718947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"24","author":[{"given":"Anying","family":"Bai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimin","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"issue":"2","key":"2397_CR1","first-page":"153","volume":"49","author":"HE Achi","year":"2019","unstructured":"Achi HE, Belousova T, Chen L, Wahed A, Wang I, Hu Z, et al. Automated diagnosis of lymphoma with digital pathology images using deep learning. Ann Clin Lab Sci. 2019;49(2):153\u201360.","journal-title":"Ann Clin Lab Sci"},{"key":"2397_CR2","unstructured":"Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396(10258):1204\u201322."},{"issue":"20","key":"2397_CR3","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1182\/blood-2016-01-643569","volume":"127","author":"SH Swerdlow","year":"2016","unstructured":"Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127(20):2375\u201390.","journal-title":"Blood."},{"issue":"3","key":"2397_CR4","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/S1052-5149(03)00039-X","volume":"13","author":"AL Weber","year":"2003","unstructured":"Weber AL, Rahemtullah A, Ferry JA. Hodgkin and non-Hodgkin lymphoma of the head and neck: clinical, pathologic, and imaging evaluation. Neuroimaging Clin N Am. 2003;13(3):371\u201392.","journal-title":"Neuroimaging Clin N Am"},{"key":"2397_CR5","doi-asserted-by":"crossref","unstructured":"Zheng Q, Yang L, Zeng B, Li J, Guo K, Liang Y, et al. Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: a systematic review and meta-analysis. EClinicalMedicine. 2021;31.","DOI":"10.1016\/j.eclinm.2020.100669"},{"issue":"3","key":"2397_CR6","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1136\/jcp.55.3.162","volume":"55","author":"S Pileri","year":"2002","unstructured":"Pileri S, Ascani S, Leoncini L, Sabattini E, Zinzani P, Piccaluga P, et al. Hodgkin's lymphoma: the pathologist's viewpoint. J Clin Pathol. 2002;55(3):162\u201376.","journal-title":"J Clin Pathol"},{"key":"2397_CR7","doi-asserted-by":"crossref","unstructured":"Mwamba PM, Mwanda WO, Busakhala N, Strother RM, Loehrer PJ, Remick SC. AIDS-related non-Hodgkin's lymphoma in sub-Saharan Africa: current status and realities of therapeutic approach. Lymphoma. 2012;2012.","DOI":"10.1155\/2012\/904367"},{"key":"2397_CR8","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1038\/s41746-020-0272-0","volume":"3","author":"C Syrykh","year":"2020","unstructured":"Syrykh C, Abreu A, Amara N, Siegfried A, Maisongrosse V, Frenois FX, et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. NPJ Digit Med. 2020;3:63.","journal-title":"NPJ Digit Med"},{"issue":"4","key":"2397_CR9","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.dsx.2020.04.012","volume":"14","author":"R Vaishya","year":"2020","unstructured":"Vaishya R, Javaid M, Khan IH, Haleem A. Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337\u20139.","journal-title":"Diabetes Metab Syndr"},{"issue":"7639","key":"2397_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115\u20138.","journal-title":"Nature"},{"issue":"5","key":"2397_CR11","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1097\/ICU.0000000000000693","volume":"31","author":"DV Gunasekeran","year":"2020","unstructured":"Gunasekeran DV, Ting DSW, Tan GSW, Wong TY. Artificial intelligence for diabetic retinopathy screening, prediction and management. Curr Opin Ophthalmol. 2020;31(5):357\u201365.","journal-title":"Curr Opin Ophthalmol"},{"issue":"9","key":"2397_CR12","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1038\/s41551-018-0265-3","volume":"2","author":"H Im","year":"2018","unstructured":"Im H, Pathania D, McFarland PJ, Sohani AR, Degani I, Allen M, et al. Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning. Nat Biomed Eng. 2018;2(9):666\u201374.","journal-title":"Nat Biomed Eng"},{"issue":"1","key":"2397_CR13","doi-asserted-by":"publisher","first-page":"6004","DOI":"10.1038\/s41467-020-19817-3","volume":"11","author":"D Li","year":"2020","unstructured":"Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, et al. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. 2020;11(1):6004.","journal-title":"Nat Commun"},{"issue":"10","key":"2397_CR14","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1038\/s41374-020-0442-3","volume":"100","author":"H Miyoshi","year":"2020","unstructured":"Miyoshi H, Sato K, Kabeya Y, Yonezawa S, Nakano H, Takeuchi Y, et al. Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma. Lab Investig. 2020;100(10):1300\u201310.","journal-title":"Lab Investig"},{"key":"2397_CR15","doi-asserted-by":"publisher","first-page":"b2535","DOI":"10.1136\/bmj.b2535","volume":"339","author":"D Moher","year":"2009","unstructured":"Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Bmj. 2009;339:b2535.","journal-title":"Bmj."},{"issue":"10","key":"2397_CR16","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","volume":"27","author":"V Sounderajah","year":"2021","unstructured":"Sounderajah V, Ashrafian H, Rose S, Shah NH, Ghassemi M, Golub R, et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med. 2021;27(10):1663\u20135.","journal-title":"Nat Med"},{"issue":"8","key":"2397_CR17","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529\u201336.","journal-title":"Ann Intern Med"},{"issue":"11","key":"2397_CR18","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.7326\/M21-2234","volume":"174","author":"B Yang","year":"2021","unstructured":"Yang B, Mallett S, Takwoingi Y, Davenport CF, Hyde CJ, Whiting PF, et al. QUADAS-C: a tool for assessing risk of Bias in comparative diagnostic accuracy studies. Ann Intern Med. 2021;174(11):1592\u20139.","journal-title":"Ann Intern Med"},{"issue":"4","key":"2397_CR19","first-page":"260","volume":"11","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Jain P, Lu Y, Macapinlac H, Wang ML, Son JB, et al. Computer-aided detection of mantle cell lymphoma on (18)F-FDG PET\/CT using a deep learning convolutional neural network. Am J Nucl Med Mol Imaging. 2021;11(4):260\u201370.","journal-title":"Am J Nucl Med Mol Imaging"},{"issue":"1","key":"2397_CR20","doi-asserted-by":"publisher","first-page":"15219","DOI":"10.1038\/s41598-021-94733-0","volume":"11","author":"M McAvoy","year":"2021","unstructured":"McAvoy M, Prieto PC, Kaczmarzyk JR, Fern\u00e1ndez IS, McNulty J, Smith T, et al. Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks. Sci Rep. 2021;11(1):15219.","journal-title":"Sci Rep"},{"issue":"8","key":"2397_CR21","doi-asserted-by":"publisher","first-page":"4475","DOI":"10.1007\/s00330-020-06794-w","volume":"30","author":"JE Park","year":"2020","unstructured":"Park JE, Ryu YJ, Kim JY, Kim YH, Park JY, Lee H, et al. Cervical lymphadenopathy in children: a diagnostic tree analysis model based on ultrasonographic and clinical findings. Eur Radiol. 2020;30(8):4475\u201385.","journal-title":"Eur Radiol"},{"issue":"6","key":"2397_CR22","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1093\/ajcp\/aqaa001","volume":"153","author":"JS Mohlman","year":"2020","unstructured":"Mohlman JS, Leventhal SD, Hansen T, Kohan J, Pascucci V, Salama ME. Improving augmented human intelligence to distinguish Burkitt lymphoma from diffuse large B-cell lymphoma cases. Am J Clin Pathol. 2020;153(6):743\u201359.","journal-title":"Am J Clin Pathol"},{"issue":"14","key":"2397_CR23","doi-asserted-by":"publisher","first-page":"307","DOI":"10.21037\/atm.2019.06.29","volume":"7","author":"Q Guan","year":"2019","unstructured":"Guan Q, Wan X, Lu H, Ping B, Li D, Wang L, et al. Deep convolutional neural network inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study. Ann Transl Med. 2019;7(14):307.","journal-title":"Ann Transl Med"},{"issue":"10","key":"2397_CR24","doi-asserted-by":"publisher","first-page":"3151","DOI":"10.1007\/s00259-021-05232-3","volume":"48","author":"R Guo","year":"2021","unstructured":"Guo R, Hu X, Song H, Xu P, Xu H, Rominger A, et al. Weakly supervised deep learning for determining the prognostic value of (18)F-FDG PET\/CT in extranodal natural killer\/T cell lymphoma, nasal type. Eur J Nucl Med Mol Imaging. 2021;48(10):3151\u201361.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"3","key":"2397_CR25","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1002\/jmri.27592","volume":"54","author":"W Xia","year":"2021","unstructured":"Xia W, Hu B, Li H, Shi W, Tang Y, Yu Y, et al. Deep learning for automatic differential diagnosis of primary central nervous system lymphoma and glioblastoma: multi-parametric magnetic resonance imaging based convolutional neural network model. J Magn Reson Imaging. 2021;54(3):880\u20137.","journal-title":"J Magn Reson Imaging"},{"key":"2397_CR26","doi-asserted-by":"publisher","first-page":"665891","DOI":"10.3389\/fonc.2021.665891","volume":"11","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Liang K, He J, Ma H, Chen H, Zheng F, et al. Deep learning with data enhancement for the differentiation of solitary and multiple cerebral glioblastoma, lymphoma, and Tumefactive demyelinating lesion. Front Oncol. 2021;11:665891.","journal-title":"Front Oncol"},{"issue":"10","key":"2397_CR27","doi-asserted-by":"publisher","first-page":"5578","DOI":"10.1007\/s00330-020-06943-1","volume":"30","author":"H Wang","year":"2020","unstructured":"Wang H, Zhao S, Li L, Tian R. Development and validation of an (18)F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer\/T cell lymphoma. Eur Radiol. 2020;30(10):5578\u201387.","journal-title":"Eur Radiol"},{"issue":"9","key":"2397_CR28","doi-asserted-by":"publisher","first-page":"4241","DOI":"10.1002\/mp.14357","volume":"47","author":"J Zhang","year":"2020","unstructured":"Zhang J, Cui W, Guo X, Wang B, Wang Z. Classification of digital pathological images of non-Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis. Med Phys. 2020;47(9):4241\u201353.","journal-title":"Med Phys"},{"issue":"6","key":"2397_CR29","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1364\/BOE.8.003017","volume":"8","author":"Q Wang","year":"2017","unstructured":"Wang Q, Wang J, Zhou M, Li Q, Wang Y. Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology. Biomed Opt Express. 2017;8(6):3017\u201328.","journal-title":"Biomed Opt Express"},{"issue":"1","key":"2397_CR30","doi-asserted-by":"publisher","first-page":"7995","DOI":"10.1038\/s41598-021-86995-5","volume":"11","author":"JPE Schouten","year":"2021","unstructured":"Schouten JPE, Matek C, Jacobs LFP, Buck MC, Bo\u0161na\u010dki D, Marr C. Tens of images can suffice to train neural networks for malignant leukocyte detection. Sci Rep. 2021;11(1):7995.","journal-title":"Sci Rep"},{"key":"2397_CR31","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.ejrad.2018.09.017","volume":"108","author":"M Nakagawa","year":"2018","unstructured":"Nakagawa M, Nakaura T, Namimoto T, Kitajima M, Uetani H, Tateishi M, et al. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma. Eur J Radiol. 2018;108:147\u201354.","journal-title":"Eur J Radiol"},{"key":"2397_CR32","doi-asserted-by":"publisher","first-page":"153303381880278","DOI":"10.1177\/1533033818802789","volume":"17","author":"S Shafique","year":"2018","unstructured":"Shafique S, Tehsin S. Acute lymphoblastic leukemia detection and classification of its subtypes using Pretrained deep convolutional neural networks. Technol Cancer Res Treat. 2018;17:1533033818802789.","journal-title":"Technol Cancer Res Treat"},{"key":"2397_CR33","doi-asserted-by":"publisher","first-page":"101912","DOI":"10.1016\/j.nicl.2019.101912","volume":"23","author":"Z Kong","year":"2019","unstructured":"Kong Z, Jiang C, Zhu R, Feng S, Wang Y, Li J, et al. (18)F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma. Neuroimage Clin. 2019;23:101912.","journal-title":"Neuroimage Clin"},{"issue":"5","key":"2397_CR34","doi-asserted-by":"publisher","first-page":"e200016","DOI":"10.1148\/ryai.2020200016","volume":"2","author":"AJ Weisman","year":"2020","unstructured":"Weisman AJ, Kieler MW, Perlman SB, Hutchings M, Jeraj R, Kostakoglu L, et al. Convolutional neural networks for automated PET\/CT detection of diseased lymph node burden in patients with lymphoma. Radiol Artif Intell. 2020;2(5):e200016.","journal-title":"Radiol Artif Intell"},{"issue":"12","key":"2397_CR35","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s00234-018-2091-4","volume":"60","author":"Y Kim","year":"2018","unstructured":"Kim Y, Cho HH, Kim ST, Park H, Nam D, Kong DS. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology. 2018;60(12):1297\u2013305.","journal-title":"Neuroradiology."},{"issue":"4","key":"2397_CR36","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1155\/2012\/983812","volume":"35","author":"M Stycze\u0144","year":"2012","unstructured":"Stycze\u0144 M, Szpor J, Demczuk S, Oko\u0144 K. Karyometric comparison of splenic and gastric marginal zone lymphomas. Anal Cell Pathol (Amst). 2012;35(4):297\u2013303.","journal-title":"Anal Cell Pathol (Amst)"},{"issue":"9","key":"2397_CR37","doi-asserted-by":"publisher","first-page":"3872","DOI":"10.1007\/s00330-018-5381-7","volume":"28","author":"J Guo","year":"2018","unstructured":"Guo J, Liu Z, Shen C, Li Z, Yan F, Tian J, et al. MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol. 2018;28(9):3872\u201381.","journal-title":"Eur Radiol"},{"issue":"3","key":"2397_CR38","doi-asserted-by":"publisher","first-page":"470","DOI":"10.25259\/JNRP_50_2022","volume":"14","author":"J Jaruenpunyasak","year":"2023","unstructured":"Jaruenpunyasak J, Duangsoithong R, Tunthanathip T. Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors. J Neurosci Rural Pract. 2023;14(3):470\u20136.","journal-title":"J Neurosci Rural Pract"},{"key":"2397_CR39","doi-asserted-by":"publisher","first-page":"101244","DOI":"10.1016\/j.imu.2023.101244","volume":"39","author":"A Hosseini","year":"2023","unstructured":"Hosseini A, Eshraghi MA, Taami T, Sadeghsalehi H, Hoseinzadeh Z, Ghaderzadeh M, et al. A mobile application based on efficient lightweight CNN model for classification of B-ALL cancer from non-cancerous cells: a design and implementation study. Informatics in Medicine Unlocked. 2023;39:101244.","journal-title":"Informatics in Medicine Unlocked"},{"issue":"21","key":"2397_CR40","doi-asserted-by":"publisher","first-page":"5205","DOI":"10.3390\/cancers15215205","volume":"15","author":"C Perry","year":"2023","unstructured":"Perry C, Greenberg O, Haberman S, Herskovitz N, Gazy I, Avinoam A, et al. Image-based deep learning detection of high-grade B-cell lymphomas directly from hematoxylin and eosin images. Cancers. 2023;15(21):5205.","journal-title":"Cancers."},{"key":"2397_CR41","doi-asserted-by":"crossref","unstructured":"Aoki H, Miyazaki Y, Anzai T, Yokoyama K, Tsuchiya J, Shirai T, et al. Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [(18)F] FDG maximum-intensity projection images. Eur Radiol. 2023;.","DOI":"10.1007\/s00330-023-09937-x"},{"key":"2397_CR42","doi-asserted-by":"crossref","unstructured":"Kaur M, AlZubi AA, Jain A, Singh D, Yadav V, Alkhayyat A. DSCNet: deep skip connections-based dense network for ALL diagnosis using peripheral blood smear images. Diagnostics (Basel). 2023;13(17).","DOI":"10.3390\/diagnostics13172752"},{"key":"2397_CR43","doi-asserted-by":"publisher","first-page":"102752","DOI":"10.1016\/j.media.2023.102752","volume":"85","author":"N Hashimoto","year":"2023","unstructured":"Hashimoto N, Takagi Y, Masuda H, Miyoshi H, Kohno K, Nagaishi M, et al. Case-based similar image retrieval for weakly annotated large histopathological images of malignant lymphoma using deep metric learning. Med Image Anal. 2023;85:102752.","journal-title":"Med Image Anal"},{"issue":"7","key":"2397_CR44","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s00198-021-05887-6","volume":"32","author":"L Gao","year":"2021","unstructured":"Gao L, Jiao T, Feng Q, Wang W. Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis. Osteoporos Int. 2021;32(7):1279\u201386.","journal-title":"Osteoporos Int"},{"issue":"1","key":"2397_CR45","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1186\/s12885-021-08773-w","volume":"21","author":"S Bedrikovetski","year":"2021","unstructured":"Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G, et al. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer. 2021;21(1):1058.","journal-title":"BMC Cancer"},{"issue":"3","key":"2397_CR46","doi-asserted-by":"publisher","first-page":"405","DOI":"10.3348\/kjr.2019.0025","volume":"20","author":"DW Kim","year":"2019","unstructured":"Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol. 2019;20(3):405\u201310.","journal-title":"Korean J Radiol"},{"key":"2397_CR47","doi-asserted-by":"publisher","first-page":"3597","DOI":"10.2147\/OTT.S148189","volume":"11","author":"D Wang","year":"2018","unstructured":"Wang D, Huo Y, Chen S, Wang H, Ding Y, Zhu X, et al. Whole-body MRI versus (18)F-FDG PET\/CT for pretherapeutic assessment and staging of lymphoma: a meta-analysis. Onco Targets Ther. 2018;11:3597\u2013608.","journal-title":"Onco Targets Ther"},{"issue":"2","key":"2397_CR48","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1148\/radiol.2312021185","volume":"231","author":"EM Rohren","year":"2004","unstructured":"Rohren EM, Turkington TG, Coleman RE. Clinical applications of PET in oncology. Radiology. 2004;231(2):305\u201332.","journal-title":"Radiology."},{"issue":"3","key":"2397_CR49","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S1470-2045(14)70021-X","volume":"15","author":"C Klenk","year":"2014","unstructured":"Klenk C, Gawande R, Uslu L, Khurana A, Qiu D, Quon A, et al. Ionising radiation-free whole-body MRI versus (18)F-fluorodeoxyglucose PET\/CT scans for children and young adults with cancer: a prospective, non-randomised, single-Centre study. Lancet Oncol. 2014;15(3):275\u201385.","journal-title":"Lancet Oncol"},{"key":"2397_CR50","doi-asserted-by":"publisher","first-page":"101662","DOI":"10.1016\/j.eclinm.2022.101662","volume":"53","author":"HL Xu","year":"2022","unstructured":"Xu HL, Gong TT, Liu FH, Chen HY, Xiao Q, Hou Y, et al. Artificial intelligence performance in image-based ovarian cancer identification: a systematic review and meta-analysis. EClinicalMedicine. 2022;53:101662.","journal-title":"EClinicalMedicine."},{"key":"2397_CR51","doi-asserted-by":"publisher","first-page":"110247","DOI":"10.1016\/j.ejrad.2022.110247","volume":"150","author":"X Liang","year":"2022","unstructured":"Liang X, Yu X, Gao T. Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis. Eur J Radiol. 2022;150:110247.","journal-title":"Eur J Radiol"},{"issue":"9","key":"2397_CR52","doi-asserted-by":"publisher","first-page":"e29080","DOI":"10.1002\/jmv.29080","volume":"95","author":"C Song","year":"2023","unstructured":"Song C, Chen X, Tang C, Xue P, Jiang Y, Qiao Y. Artificial intelligence for HPV status prediction based on disease-specific images in head and neck cancer: a systematic review and meta-analysis. J Med Virol. 2023;95(9):e29080.","journal-title":"J Med Virol"},{"issue":"2","key":"2397_CR53","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29(2):102\u201327.","journal-title":"Z Med Phys"},{"issue":"8","key":"2397_CR54","doi-asserted-by":"publisher","first-page":"e22961","DOI":"10.1002\/jcla.22961","volume":"33","author":"K Liu","year":"2019","unstructured":"Liu K, Hu J, Wang X, Li L. Chronic myeloid leukemia blast crisis presented with AML of t(9;22) and t(3;14) mimicking acute lymphocytic leukemia. J Clin Lab Anal. 2019;33(8):e22961.","journal-title":"J Clin Lab Anal"},{"issue":"1","key":"2397_CR55","first-page":"e21077","volume":"14","author":"H Arzoun","year":"2022","unstructured":"Arzoun H, Srinivasan M, Thangaraj SR, Thomas SS, Mohammed L. The progression of chronic myeloid leukemia to myeloid sarcoma: a systematic review. Cureus. 2022;14(1):e21077.","journal-title":"Cureus."},{"key":"2397_CR56","doi-asserted-by":"crossref","unstructured":"Steinbuss G, Kriegsmann M, Zgorzelski C, Brobeil A, Goeppert B, Dietrich S, et al. Deep learning for the classification of non-Hodgkin lymphoma on histopathological images. Cancers (Basel). 2021;13(10).","DOI":"10.3390\/cancers13102419"},{"issue":"9","key":"2397_CR57","doi-asserted-by":"publisher","first-page":"e419","DOI":"10.1016\/S2468-2667(18)30135-X","volume":"3","author":"SB Seidelmann","year":"2018","unstructured":"Seidelmann SB, Claggett B, Cheng S, Henglin M, Shah A, Steffen LM, et al. Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis. Lancet Public Health. 2018;3(9):e419\u2013e28.","journal-title":"Lancet Public Health"},{"issue":"6","key":"2397_CR58","doi-asserted-by":"publisher","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","volume":"1","author":"X Liu","year":"2019","unstructured":"Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The lancet digital health. 2019;1(6):e271\u2013e97.","journal-title":"The lancet digital health"},{"key":"2397_CR59","doi-asserted-by":"crossref","unstructured":"Yadav S, Shukla S. Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. 2016 IEEE 6th International Conference on Advanced Computing (IACC); 2016 27\u201328 Feb. 2016.","DOI":"10.1109\/IACC.2016.25"},{"issue":"3","key":"2397_CR60","doi-asserted-by":"publisher","first-page":"513","DOI":"10.2214\/AJR.18.20490","volume":"212","author":"JR England","year":"2019","unstructured":"England JR, Cheng PM. Artificial intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol. 2019;212(3):513\u20139.","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"2397_CR61","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1148\/radiol.2017171920","volume":"286","author":"SH Park","year":"2018","unstructured":"Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence Technology for Medical Diagnosis and Prediction. Radiology. 2018;286(3):800\u20139.","journal-title":"Radiology."},{"issue":"22","key":"2397_CR62","doi-asserted-by":"publisher","first-page":"e152","DOI":"10.3346\/jkms.2018.33.e152","volume":"33","author":"SH Park","year":"2018","unstructured":"Park SH, Kressel HY. Connecting technological innovation in artificial intelligence to real-world medical practice through rigorous clinical validation: what peer-reviewed medical journals could do. J Korean Med Sci. 2018;33(22):e152.","journal-title":"J Korean Med Sci"},{"key":"2397_CR63","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj. 2021;372:n71.","journal-title":"Bmj."},{"issue":"3","key":"2397_CR64","doi-asserted-by":"publisher","first-page":"e064739","DOI":"10.1136\/bmjopen-2022-064739","volume":"13","author":"GE Fowler","year":"2023","unstructured":"Fowler GE, Blencowe NS, Hardacre C, Callaway MP, Smart NJ, Macefield R. Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of surgical pathology in the abdominopelvic cavity: a systematic review. BMJ Open. 2023;13(3):e064739.","journal-title":"BMJ Open"},{"key":"2397_CR65","doi-asserted-by":"publisher","first-page":"e365","DOI":"10.5114\/pjr.2023.130815","volume":"88","author":"S Heydarheydari","year":"2023","unstructured":"Heydarheydari S, Birgani MJT, Rezaeijo SM. Auto-segmentation of head and neck tumors in positron emission tomography images using non-local means and morphological frameworks. Pol J Radiol. 2023;88:e365\u2013e70.","journal-title":"Pol J Radiol"},{"key":"2397_CR66","doi-asserted-by":"crossref","unstructured":"Hosseinzadeh M, Gorji A, Fathi Jouzdani A, Rezaeijo SM, Rahmim A, Salmanpour MR. Prediction of cognitive decline in Parkinson's disease using clinical and DAT SPECT imaging features, and hybrid machine learning systems. Diagnostics (Basel). 2023;13(10).","DOI":"10.3390\/diagnostics13101691"},{"key":"2397_CR67","doi-asserted-by":"publisher","first-page":"h5527","DOI":"10.1136\/bmj.h5527","volume":"351","author":"PM Bossuyt","year":"2015","unstructured":"Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Bmj. 2015;351:h5527.","journal-title":"Bmj."},{"issue":"1","key":"2397_CR68","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s41746-022-00559-z","volume":"5","author":"P Xue","year":"2022","unstructured":"Xue P, Wang J, Qin D, Yan H, Qu Y, Seery S, et al. Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis. NPJ Digit Med. 2022;5(1):19.","journal-title":"NPJ Digit Med"},{"key":"2397_CR69","doi-asserted-by":"publisher","first-page":"107714","DOI":"10.1016\/j.cmpb.2023.107714","volume":"240","author":"MR Salmanpour","year":"2023","unstructured":"Salmanpour MR, Hosseinzadeh M, Rezaeijo SM, Rahmim A. Fusion-based tensor radiomics using reproducible features: application to survival prediction in head and neck cancer. Comput Methods Prog Biomed. 2023;240:107714.","journal-title":"Comput Methods Prog Biomed"},{"key":"2397_CR70","doi-asserted-by":"crossref","unstructured":"Rezaeijo SM, Chegeni N, Baghaei Naeini F, Makris D, Bakas S. Within-modality synthesis and novel Radiomic evaluation of brain MRI scans. Cancers (Basel). 2023;15(14).","DOI":"10.3390\/cancers15143565"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02397-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-023-02397-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02397-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T13:02:40Z","timestamp":1704718960000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-023-02397-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2397"],"URL":"https:\/\/doi.org\/10.1186\/s12911-023-02397-9","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]},"assertion":[{"value":"1 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"13"}}