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For: Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R. Deep learning-enabled medical computer vision. NPJ Digit Med. 2021;4:5. [PMID: 33420381 DOI: 10.1038/s41746-020-00376-2] [Cited by in Crossref: 16] [Cited by in F6Publishing: 9] [Article Influence: 16.0] [Reference Citation Analysis]
Number Citing Articles
1 Danta M, Dreyer P, Bezerra D, Reis G, Souza R, Lins S, Kelner J, Sadok D. Video object segmentation for automatic image annotation of ethernet connectors with environment mapping and 3D projection. Multimed Tools Appl. [DOI: 10.1007/s11042-022-13128-z] [Reference Citation Analysis]
2 Li S, Hickey GW, Lander MM, Kanwar MK. Artificial Intelligence and Mechanical Circulatory Support. Heart Failure Clinics 2022. [DOI: 10.1016/j.hfc.2021.11.005] [Reference Citation Analysis]
3 Shad R, Cunningham JP, Ashley EA, Langlotz CP, Hiesinger W. Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. Nat Mach Intell 2021;3:929-35. [DOI: 10.1038/s42256-021-00399-8] [Reference Citation Analysis]
4 Nachmani O, Saun T, Huynh M, Forrest CR, Mcrae M. “Facekit”—Toward an Automated Facial Analysis App Using a Machine Learning–Derived Facial Recognition Algorithm. Plast Surg (Oakv). [DOI: 10.1177/22925503211073843] [Reference Citation Analysis]
5 Allam A, Feuerriegel S, Rebhan M, Krauthammer M. Analyzing Patient Trajectories With Artificial Intelligence. J Med Internet Res 2021;23:e29812. [PMID: 34870606 DOI: 10.2196/29812] [Reference Citation Analysis]
6 Barros B, Lacerda P, Albuquerque C, Conci A. Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification. Sensors (Basel) 2021;21:5486. [PMID: 34450928 DOI: 10.3390/s21165486] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Hallou A, Yevick HG, Dumitrascu B, Uhlmann V. Deep learning for bioimage analysis in developmental biology. Development 2021;148:dev199616. [PMID: 34490888 DOI: 10.1242/dev.199616] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Theodorakopoulos I, Fotopoulou F, Economou G. Geometric Regularization of Local Activations for Knowledge Transfer in Convolutional Neural Networks. Information 2021;12:333. [DOI: 10.3390/info12080333] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Ovalle-magallanes E, Avina-cervantes JG, Cruz-aceves I, Ruiz-pinales J. Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images. Computer Methods and Programs in Biomedicine 2022;219:106767. [DOI: 10.1016/j.cmpb.2022.106767] [Reference Citation Analysis]
10 Dunnmon J. Separating Hope from Hype: Artificial Intelligence Pitfalls and Challenges in Radiology. Radiol Clin North Am 2021;59:1063-74. [PMID: 34689874 DOI: 10.1016/j.rcl.2021.07.006] [Reference Citation Analysis]
11 Rashmi R, Snekhalatha U, Krishnan PT, Dhanraj V. Fat-based studies for computer-assisted screening of child obesity using thermal imaging based on deep learning techniques: a comparison with quantum machine learning approach. Soft Comput. [DOI: 10.1007/s00500-021-06668-3] [Reference Citation Analysis]
12 Bao S, Li K, Yan C, Zhang Z, Qu J, Zhou M. Deep learning-based advances and applications for single-cell RNA-sequencing data analysis. Brief Bioinform 2021:bbab473. [PMID: 34849562 DOI: 10.1093/bib/bbab473] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Keenan TDL, Chen Q, Agrón E, Tham YC, Lin Goh JH, Lei X, Ng YP, Liu Y, Xu X, Cheng CY, Bikbov MM, Jonas JB, Bhandari S, Broadhead GK, Colyer MH, Corsini J, Cousineau-Krieger C, Gensheimer W, Grasic D, Lamba T, Magone MT, Maiberger M, Oshinsky A, Purt B, Shin SY, Thavikulwat AT, Lu Z, Chew EY; AREDS Deep Learning Research Group. Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity. Ophthalmology 2022:S0161-6420(21)00967-2. [PMID: 34990643 DOI: 10.1016/j.ophtha.2021.12.017] [Reference Citation Analysis]
14 Gandhi D, Garg T, Patel L, Elkassem AA, Bansal V, Smith A. Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes. Clinical Imaging 2022. [DOI: 10.1016/j.clinimag.2022.04.007] [Reference Citation Analysis]
15 Lamberti WF. Blood cell classification using interpretable shape features: A Comparative study of SVM models and CNN-Based approaches. Computer Methods and Programs in Biomedicine Update 2021;1:100023. [DOI: 10.1016/j.cmpbup.2021.100023] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Maron RC, Schlager JG, Haggenmüller S, von Kalle C, Utikal JS, Meier F, Gellrich FF, Hobelsberger S, Hauschild A, French L, Heinzerling L, Schlaak M, Ghoreschi K, Hilke FJ, Poch G, Heppt MV, Berking C, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Goebeler M, Krieghoff-Henning E, Hekler A, Fröhling S, Lipka DB, Kather JN, Brinker TJ. A benchmark for neural network robustness in skin cancer classification. Eur J Cancer 2021;155:191-9. [PMID: 34388516 DOI: 10.1016/j.ejca.2021.06.047] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Trovato G, Russo M. Artificial Intelligence (AI) and Lung Ultrasound in Infectious Pulmonary Disease. Front Med (Lausanne) 2021;8:706794. [PMID: 34901048 DOI: 10.3389/fmed.2021.706794] [Reference Citation Analysis]
18 Voigt I, Boeckmann M, Bruder O, Wolf A, Schmitz T, Wieneke H. A deep neural network using audio files for detection of aortic stenosis. Clin Cardiol 2022. [PMID: 35438211 DOI: 10.1002/clc.23826] [Reference Citation Analysis]
19 Kumar SA, Ananda Kumar TD, Beeraka NM, Pujar GV, Singh M, Narayana Akshatha HS, Bhagyalalitha M. Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Reference Citation Analysis]
20 Kader R, Baggaley RF, Hussein M, Ahmad OF, Patel N, Corbett G, Dolwani S, Stoyanov D, Lovat LB. Survey on the perceptions of UK gastroenterologists and endoscopists to artificial intelligence. Frontline Gastroenterol. [DOI: 10.1136/flgastro-2021-101994] [Reference Citation Analysis]
21 Nguyen-Vo TH, Trinh QH, Nguyen L, Nguyen-Hoang PU, Nguyen TN, Nguyen DT, Nguyen BP, Le L. iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding. J Chem Inf Model 2021. [PMID: 34672553 DOI: 10.1021/acs.jcim.1c00628] [Reference Citation Analysis]
22 Jumah F, Raju B, Nagaraj A, Shinde R, Lescott C, Sun H, Gupta G, Nanda A. Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery. World Neurosurg 2022;160:4-12. [PMID: 35026457 DOI: 10.1016/j.wneu.2022.01.020] [Reference Citation Analysis]
23 Ravi V, Narasimhan H, Chakraborty C, Pham TD. Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images. Multimed Syst 2021;:1-15. [PMID: 34248292 DOI: 10.1007/s00530-021-00826-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
24 González-Gonzalo C, Thee EF, Klaver CCW, Lee AY, Schlingemann RO, Tufail A, Verbraak F, Sánchez CI. Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice. Prog Retin Eye Res 2021;:101034. [PMID: 34902546 DOI: 10.1016/j.preteyeres.2021.101034] [Reference Citation Analysis]
25 Yang L, Wang SH, Zhang YD. EDNC: Ensemble Deep Neural Network for COVID-19 Recognition. Tomography 2022;8:869-90. [PMID: 35314648 DOI: 10.3390/tomography8020071] [Reference Citation Analysis]
26 Ravi V, Narasimhan H, Pham TD. A cost‐sensitive deep learning‐based meta‐classifier for pediatric pneumonia classification using chest X‐rays. Expert Systems. [DOI: 10.1111/exsy.12966] [Reference Citation Analysis]
27 Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction . Artif Intell Gastroenterol 2021; 2(2): 56-68 [DOI: 10.35712/aig.v2.i2.56] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Zhou Z, Yu L, Tian S, Xiao G. Diagnosis of Alzheimer’s disease using 2D dynamic magnetic resonance imaging. J Ambient Intell Human Comput. [DOI: 10.1007/s12652-021-03678-9] [Reference Citation Analysis]
29 Guérinot C, Marcon V, Godard C, Blanc T, Verdier H, Planchon G, Raimondi F, Boddaert N, Alonso M, Sailor K, Lledo P, Hajj B, El Beheiry M, Masson J. New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing. Front Bioinform 2022;1:777101. [DOI: 10.3389/fbinf.2021.777101] [Reference Citation Analysis]
30 Balluet M, Sizaire F, El Habouz Y, Walter T, Pont J, Giroux B, Bouchareb O, Tramier M, Pecreaux J. Neural network fast-classifies biological images through features selecting to power automated microscopy. J Microsc 2021. [PMID: 34623634 DOI: 10.1111/jmi.13062] [Reference Citation Analysis]
31 Hügle T, Kalweit M. [Artificial intelligence-supported treatment in rheumatology : Principles, current situation and perspectives]. Z Rheumatol 2021;80:914-27. [PMID: 34618208 DOI: 10.1007/s00393-021-01096-y] [Reference Citation Analysis]
32 Chan EJJ, Najjar RP, Tang Z, Milea D. Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders. Asia Pac J Ophthalmol (Phila) 2021;10:282-8. [PMID: 34383719 DOI: 10.1097/APO.0000000000000404] [Reference Citation Analysis]
33 Haleem A, Javaid M, Singh RP, Suman R. Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. Sustainable Operations and Computers 2021;2:71-8. [DOI: 10.1016/j.susoc.2021.04.003] [Cited by in Crossref: 5] [Article Influence: 5.0] [Reference Citation Analysis]
34 Vajen B, Hänselmann S, Lutterloh F, Käfer S, Espenkötter J, Beening A, Bogin J, Schlegelberger B, Göhring G. Classification of fluorescent R-Band metaphase chromosomes using a convolutional neural network is precise and fast in generating karyograms of hematologic neoplastic cells. Cancer Genet 2022;260-261:23-9. [PMID: 34839233 DOI: 10.1016/j.cancergen.2021.11.005] [Reference Citation Analysis]
35 Kugener G, Zhu Y, Pangal DJ, Sinha A, Markarian N, Roshannai A, Chan J, Anandkumar A, Hung AJ, Wrobel BB, Zada G, Donoho DA. Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success From Video. Neurosurgery 2022. [PMID: 35319539 DOI: 10.1227/neu.0000000000001906] [Reference Citation Analysis]
36 Jones CM, Danaher L, Milne MR, Tang C, Seah J, Oakden-Rayner L, Johnson A, Buchlak QD, Esmaili N. Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study. BMJ Open 2021;11:e052902. [PMID: 34930738 DOI: 10.1136/bmjopen-2021-052902] [Reference Citation Analysis]
37 Acar E, Şahin E, Yılmaz İ. Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images. Neural Comput Appl 2021;:1-21. [PMID: 34345118 DOI: 10.1007/s00521-021-06344-5] [Reference Citation Analysis]
38 Liu FY, Chen CC, Cheng CT, Wu CT, Hsu CP, Fu CY, Chen SC, Liao CH, Lee MS. Automatic Hip Detection in Anteroposterior Pelvic Radiographs-A Labelless Practical Framework. J Pers Med 2021;11:522. [PMID: 34200151 DOI: 10.3390/jpm11060522] [Reference Citation Analysis]
39 Chen X, Chen Z, Xu D, Lyu Y, Li Y, Li S, Wang J, Wang Z. De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis. Front Bioeng Biotechnol 2021;9:694100. [PMID: 34195182 DOI: 10.3389/fbioe.2021.694100] [Reference Citation Analysis]
40 Horry M, Chakraborty S, Pradhan B, Paul M, Gomes D, Ul-Haq A, Alamri A. Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images. Sensors (Basel) 2021;21:6655. [PMID: 34640976 DOI: 10.3390/s21196655] [Reference Citation Analysis]
41 Krishnamurthy S, Srinivasan K, Qaisar SM, Vincent PMDR, Chang CY. Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis. Comput Math Methods Med 2021;2021:8036304. [PMID: 34552660 DOI: 10.1155/2021/8036304] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]