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For: Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-rivero AI, Etmann C, Mccague C, Beer L, Weir-mccall JR, Teng Z, Gkrania-klotsas E, Rudd JHF, Sala E, Schönlieb C; AIX-COVNET. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 2021;3:199-217. [DOI: 10.1038/s42256-021-00307-0] [Cited by in Crossref: 282] [Cited by in F6Publishing: 298] [Article Influence: 282.0] [Reference Citation Analysis]
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17 Sivanesan U, Wu K, Mcinnes MDF, Dhindsa K, Salehi F, van der Pol CB. Checklist for Artificial Intelligence in Medical Imaging Reporting Adherence in Peer-Reviewed and Preprint Manuscripts With the Highest Altmetric Attention Scores: A Meta-Research Study. Can Assoc Radiol J 2022. [DOI: 10.1177/08465371221134056] [Reference Citation Analysis]
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20 Segall RS, Sankarasubbu V. Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases. International Journal of Artificial Intelligence and Machine Learning 2022;12:1-30. [DOI: 10.4018/ijaiml.313574] [Reference Citation Analysis]
21 Purcell CR, Walsh AJ, Colefax AP, Butcher P. Assessing the ability of deep learning techniques to perform real-time identification of shark species in live streaming video from drones. Front Mar Sci 2022;9:981897. [DOI: 10.3389/fmars.2022.981897] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Althenayan AS, Alsalamah SA, Aly S, Nouh T, Mirza AA. Detection and Classification of COVID-19 by Radiological Imaging Modalities Using Deep Learning Techniques: A Literature Review. Applied Sciences 2022;12:10535. [DOI: 10.3390/app122010535] [Reference Citation Analysis]
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24 García-Jacas CR, García-González LA, Martinez-Rios F, Tapia-Contreras IP, Brizuela CA. Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant? Brief Bioinform 2022:bbac428. [PMID: 36215083 DOI: 10.1093/bib/bbac428] [Reference Citation Analysis]
25 Fadja AN, Fraccaroli M, Bizzarri A, Mazzuchelli G, Lamma E. Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients. Med Biol Eng Comput 2022;60:3461-74. [PMID: 36201136 DOI: 10.1007/s11517-022-02674-1] [Reference Citation Analysis]
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28 Islam W, Jones M, Faiz R, Sadeghipour N, Qiu Y, Zheng B. Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism. Tomography 2022;8:2411-2425. [DOI: 10.3390/tomography8050200] [Reference Citation Analysis]
29 Peng L, Wang C, Tian G, Liu G, Li G, Lu Y, Yang J, Chen M, Li Z. Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet. Front Microbiol 2022;13:995323. [DOI: 10.3389/fmicb.2022.995323] [Reference Citation Analysis]
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32 Anibal J, Landa A, Nguyen H, Peltekian A, Shin A, Christou A, Hazen L, Song M, Rivera J, Morhard R, Bagci U, Li M, Clifton D, Wood B. Social Media Data for Omicron Detection from Unscripted Voice Samples.. [DOI: 10.1101/2022.09.13.22279673] [Reference Citation Analysis]
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34 Roth HR, Xu Z, Tor-Díez C, Sanchez Jacob R, Zember J, Molto J, Li W, Xu S, Turkbey B, Turkbey E, Yang D, Harouni A, Rieke N, Hu S, Isensee F, Tang C, Yu Q, Sölter J, Zheng T, Liauchuk V, Zhou Z, Moltz JH, Oliveira B, Xia Y, Maier-Hein KH, Li Q, Husch A, Zhang L, Kovalev V, Kang L, Hering A, Vilaça JL, Flores M, Xu D, Wood B, Linguraru MG. Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge. Med Image Anal 2022;82:102605. [PMID: 36156419 DOI: 10.1016/j.media.2022.102605] [Reference Citation Analysis]
35 Iqbal JD, Biller-andorno N. The regulatory gap in digital health and alternative pathways to bridge it. Health Policy and Technology 2022;11:100663. [DOI: 10.1016/j.hlpt.2022.100663] [Reference Citation Analysis]
36 Karande P, Gallagher B, Han TY. A Strategic Approach to Machine Learning for Material Science: How to Tackle Real-World Challenges and Avoid Pitfalls. Chem Mater . [DOI: 10.1021/acs.chemmater.2c01333] [Reference Citation Analysis]
37 Rouzrokh P, Khosravi B, Faghani S, Moassefi M, Vera Garcia DV, Singh Y, Zhang K, Conte GM, Erickson BJ. Mitigating Bias in Radiology Machine Learning: 1. Data Handling. Radiology: Artificial Intelligence 2022;4:e210290. [DOI: 10.1148/ryai.210290] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
38 Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol 2022;12:980793. [DOI: 10.3389/fonc.2022.980793] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Antúnez-muiños P, Vicente-palacios V, Pérez-sánchez P, Sampedro-gómez J, Sánchez-puente A, Dorado-díaz PI, Nombela-franco L, Salinas P, Gutiérrez-garcía H, Amat-santos I, Peral V, Morcuende A, Asmarats L, Freixa X, Regueiro A, Caneiro-queija B, Estevez-loureiro R, Rodés-cabau J, Sánchez PL, Cruz-gonzález I. Predictive Power for Thrombus Detection after Atrial Appendage Closure: Machine Learning vs. Classical Methods. JPM 2022;12:1413. [DOI: 10.3390/jpm12091413] [Reference Citation Analysis]
40 Shimovolos S, Shushko A, Belyaev M, Shirokikh B. Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency. J Imaging 2022;8:234. [DOI: 10.3390/jimaging8090234] [Reference Citation Analysis]
41 Palmisano A, Vignale D, Boccia E, Nonis A, Gnasso C, Leone R, Montagna M, Nicoletti V, Bianchi AG, Brusamolino S, Dorizza A, Moraschini M, Veettil R, Cereda A, Toselli M, Giannini F, Loffi M, Patelli G, Monello A, Iannopollo G, Ippolito D, Mancini EM, Pontone G, Vignali L, Scarnecchia E, Iannacone M, Baffoni L, Sperandio M, de Carlini CC, Sironi S, Rapezzi C, Antiga L, Jagher V, Di Serio C, Furlanello C, Tacchetti C, Esposito A. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients. Radiol med 2022;127:960-972. [DOI: 10.1007/s11547-022-01518-0] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
42 Gozzi N, Giacomello E, Sollini M, Kirienko M, Ammirabile A, Lanzi P, Loiacono D, Chiti A. Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs. Diagnostics 2022;12:2084. [DOI: 10.3390/diagnostics12092084] [Reference Citation Analysis]
43 Cao L, Liu Q. COVID-19 Modeling: A Review.. [DOI: 10.1101/2022.08.22.22279022] [Reference Citation Analysis]
44 Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology. J Digit Imaging 2022. [PMID: 35995898 DOI: 10.1007/s10278-022-00683-y] [Reference Citation Analysis]
45 Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, Héjja-Brichard Y, Herbst SK, Jerbi K, Kovic V, Lehongre K, Luck SJ, Mercier M, Mosher JC, Pavlov YG, Puce A, Schettino A, Schön D, Sinnott-Armstrong W, Somon B, Šoškić A, Styles SJ, Tibon R, Vilas MG, van Vliet M, Chaumon M. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage 2022;257:119056. [PMID: 35283287 DOI: 10.1016/j.neuroimage.2022.119056] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
46 Vliegenthart R, Fouras A, Jacobs C, Papanikolaou N. Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry. Respirology 2022. [PMID: 35965430 DOI: 10.1111/resp.14344] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 Chamberlin JH, Aquino G, Nance S, Wortham A, Leaphart N, Paladugu N, Brady S, Baird H, Fiegel M, Fitzpatrick L, Kocher M, Ghesu F, Mansoor A, Hoelzer P, Zimmermann M, James WE, Dennis DJ, Houston BA, Kabakus IM, Baruah D, Schoepf UJ, Burt JR. Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning. BMC Infect Dis 2022;22:637. [PMID: 35864468 DOI: 10.1186/s12879-022-07617-7] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Ravi V, Acharya V, Alazab M. A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images. Cluster Comput. [DOI: 10.1007/s10586-022-03664-6] [Reference Citation Analysis]
49 Alabed S, Maiter A, Salehi M, Mahmood A, Daniel S, Jenkins S, Goodlad M, Sharkey M, Mamalakis M, Rakocevic V, Dwivedi K, Assadi H, Wild JM, Lu H, O’regan DP, van der Geest RJ, Garg P, Swift AJ. Quality of reporting in AI cardiac MRI segmentation studies – A systematic review and recommendations for future studies. Front Cardiovasc Med 2022;9. [DOI: 10.3389/fcvm.2022.956811] [Reference Citation Analysis]
50 Jalalifar SA, Sadeghi-naini A. Data-Efficient Training of Pure Vision Transformers for the Task of Chest X-ray Abnormality Detection Using Knowledge Distillation. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022. [DOI: 10.1109/embc48229.2022.9871372] [Reference Citation Analysis]
51 Kothari R, Chiu C, Moukheiber M, Jehiro M, Bishara A, Lee C, Piracchio R, Celi LA. A descriptive appraisal of quality of reporting in a cohort of machine learning studies in anesthesiology. Anaesth Crit Care Pain Med 2022;41:101126. [PMID: 35811037 DOI: 10.1016/j.accpm.2022.101126] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
52 Lee JH, Ahn JS, Chung MJ, Jeong YJ, Kim JH, Lim JK, Kim JY, Kim YJ, Lee JE, Kim EY. Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort. Sensors 2022;22:5007. [DOI: 10.3390/s22135007] [Reference Citation Analysis]
53 Nicol ED, Weir-mccall JR, Shaw LJ, Williamson E. Great Debates in Cardiac Computed Tomography: OPINION: "Artificial Intelligence and the future of cardiovascular CT – managing expectation and challenging hype”. Journal of Cardiovascular Computed Tomography 2022. [DOI: 10.1016/j.jcct.2022.07.005] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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55 Sun J, Peng L, Li T, Adila D, Zaiman Z, Melton-meaux GB, Ingraham NE, Murray E, Boley D, Switzer S, Burns JL, Huang K, Allen T, Steenburg SD, Gichoya JW, Kummerfeld E, Tignanelli CJ. Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study. Radiology: Artificial Intelligence 2022;4:e210217. [DOI: 10.1148/ryai.210217] [Reference Citation Analysis]
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57 Luo K, Chen X, Zheng H, Shi Z. A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries. Journal of Energy Chemistry 2022. [DOI: 10.1016/j.jechem.2022.06.049] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
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61 Routhier E, Mozziconacci J. Genomics enters the deep learning era. PeerJ 2022;10:e13613. [PMID: 35769139 DOI: 10.7717/peerj.13613] [Reference Citation Analysis]
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66 Marotta A. When AI Is Wrong: Addressing Liability Challenges in Women’s Healthcare. Journal of Computer Information Systems. [DOI: 10.1080/08874417.2022.2089773] [Reference Citation Analysis]
67 Suganyadevi S, Seethalakshmi V. CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network. Wirel Pers Commun 2022;:1-25. [PMID: 35756172 DOI: 10.1007/s11277-022-09864-y] [Reference Citation Analysis]
68 Blake N, Gaifulina R, Griffin LD, Bell IM, Thomas GMH. Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature. Diagnostics 2022;12:1491. [DOI: 10.3390/diagnostics12061491] [Reference Citation Analysis]
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72 Tao Y, Yang C, Wang T, Coltey E, Jin Y, Liu Y, Jiang R, Fan Z, Song X, Shibasaki R, Chen S, Shyu M, Luis S. A Survey on Data-Driven COVID-19 and Future Pandemic Management. ACM Comput Surv . [DOI: 10.1145/3542818] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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