Ressources spécifiques à une spécialité
Le CAR est redevable à Eric Topol qui a compilé cette liste d'articles de pointe sur l'apprentissage automatique dans les spécialités qui dépendent de l'imagerie médicale.
Radiologie/Neurologie
Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018). Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine, 1(1). doi:10.1038/s41746-017-0015-z https://www.nature.com/articles/s41746-017-0015-z
Capper, D., Jones, D. T. W., Sill, M., Hovestadt, V., Schrimpf, D., Sturm, D., Pfister, S. M. (2018). DNA methylation-based classification of central nervous system tumours. Nature, 555(7697), 469-474. doi:10.1038/nature26000 https://www.nature.com/articles/nature26000
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Warier, P. (2018). Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet. doi:10.1016/s0140-6736(18)31645-3 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31645-3/fulltext
Titano, J. J., Badgeley, M., Schefflein, J., Pain, M., Su, A., Cai, M., . . . Oermann, E. K. (2018). Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med, 24(9), 1337-1341. doi:10.1038/s41591-018-0147-y https://www.nature.com/articles/s41591-018-0147-y
Pathologie
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyo, D., Tsirigos, A. (2018). Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 24(10), 1559-1567. doi:10.1038/s41591-018-0177-5 https://www.nature.com/articles/s41591-018-0177-5
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., . . . Venancio, R. (2017). Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA, 318(22), 2199-2210. doi:10.1001/jama.2017.14585 https://jamanetwork.com/journals/jama/fullarticle/2665774
Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Stumpe, M. C. (2018). Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection. Arch Pathol Lab Med. doi:10.5858/arpa.2018-0147-OA http://www.archivesofpathology.org/doi/10.5858/arpa.2018-0147-OA?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed
Nam, J. G., Park, S., Hwang, E. J., Lee, J. H., Jin, K. N., Lim, K. Y., . . . Park, C. M. (2018). Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology, 180237. doi:10.1148/radiol.2018180237 https://pubs.rsna.org/doi/10.1148/radiol.2018180237
Singh, R., Kalra, M. K., Nitiwarangkul, C., Patti, J. A., Homayounieh, F., Padole, A., Digumarthy, S. R. (2018). Deep learning in chest radiography: Detection of findings and presence of change. PLoS One, 13(10), e0204155. doi:10.1371/journal.pone.0204155 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204155
Steiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Stumpe, M. C. (2018). Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. The American Journal of Surgical Pathology, Publish Ahead of Print. doi:10.1097/pas.0000000000001151 https://journals.lww.com/ajsp/Fulltext/2018/12000/Impact_of_Deep_Learning_Assistance_on_the.7.aspx
Dermatologie
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. doi:10.1038/nature21056 https://www.nature.com/articles/nature21056
Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., level, I. I. G. (2018). Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol, 29(8), 1836-1842. doi:10.1093/annonc/mdy166 https://www.annalsofoncology.org/article/S0923-7534(19)34105-5/fulltext
Han, S. S., Kim, M. S., Lim, W., Park, G. H., Park, I., & Chang, S. E. (2018). Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol, 138(7), 1529-1538. doi:10.1016/j.jid.2018.01.028 https://www.sciencedirect.com/science/article/pii/S0022202X18301118?via%3Dihub
Ophtalmologie
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0040-6 https://www.nature.com/articles/s41746-018-0040-6
Brown, J. M., Campbell, J. P., Beers, A., Chang, K., Ostmo, S., Chan, R. V. P., Informatics in Retinopathy of Prematurity Research, C. (2018). Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmol, 136(7), 803-810. doi:10.1001/jamaophthalmol.2018.1934 https://jamanetwork.com/journals/jamaophthalmology/fullarticle/2680579
Burlina, P., Joshi, N., Pacheco, K. D., Freund, D. E., Kong, J., & Bressler, N. M. (2018). Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration. JAMA Ophthalmol. doi:10.1001/jamaophthalmol.2018.3799 https://jamanetwork.com/journals/jamaophthalmology/fullarticle/2698945
Burlina, P. M., Joshi, N., Pekala, M., Pacheco, K. D., Freund, D. E., & Bressler, N. M. (2017). Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmol, 135(11), 1170-1176. doi:10.1001/jamaophthalmol.2017.3782 https://jamanetwork.com/journals/jamaophthalmology/fullarticle/2654969
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6 https://www.nature.com/articles/s41591-018-0107-6
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402-2410. doi:10.1001/jama.2016.17216 https://jamanetwork.com/journals/jama/fullarticle/2588763
Kanagasingam, Y., Xiao, D., Vignarajan, J., Preetham, A., Tay-Kearney, M.-L., & Mehrotra, A. (2018). Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care. JAMA Network Open, 1(5). doi:10.1001/jamanetworkopen.2018.2665 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2703944
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., Zhang, K. (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 172(5), 1122-1131 e1129. doi:10.1016/j.cell.2018.02.010 https://www.sciencedirect.com/science/article/pii/S0092867418301545?via%3Dihub
Long, E., Lin, H., Liu, Z., Wu, X., Wang, L., Jiang, J., Liu, Y. (2017). An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature Biomedical Engineering, 1(2). doi:10.1038/s41551-016-0024 https://www.nature.com/articles/s41551-016-0024
Gastro-entérologie
Mori, Y., Kudo, S. E., Misawa, M., Saito, Y., Ikematsu, H., Hotta, K., Mori, K. (2018). Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study. Ann Intern Med, 169(6), 357-366. doi:10.7326/M18-0249 https://www.acpjournals.org/doi/10.7326/M18-0249
Cardiologie
Madani, A., Arnaout, R., Mofrad, M., & Arnaout, R. (2018). Fast and accurate view classification of echocardiograms using deep learning. npj Digital Medicine, 1(1). doi:10.1038/s41746-017-0013-1 https://www.nature.com/articles/s41746-017-0013-1
Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Deo, R. C. (2018). Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation, 138(16), 1623-1635. doi:10.1161/circulationaha.118.034338 https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.034338