Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods

EBioMedicine - Tập 27 - Trang 94-102 - 2018
Xiangyi Kong1,2, Shun Gong3, Lijuan Su4,5, Newton Howard6,7, Yanguo Kong1
1Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Hutong, Dongcheng District, Beijing 100730, China
2Department of Breast Surgical Oncology, China National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Chaoyangqu, Panjiayuan-Nanli 17, Beijing 100021, PR China
3Department of Neurosurgery, Shanghai Institute of Neurosurgery, PLA Institute of Neurosurgery, Changzheng Hospital, Second Military Medical University, 415 Fengyang Road, Shanghai 200003, China
4College of Computer Science and Technology, Zhejiang University, No. 38, Zheda Road, Hangzhou, Zhejiang 310027, China
5Healthcare big data lab, Tencent Technology (Shenzhen) Company Limited, Kejizhongyi Avenue, Hi-tech Park, Nanshan District, Shenzhen, 518057, China
6Synthetic Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
7Computational Neuroscience Laboratory, Oxford University, Oxford OX1 3QD, UK

Tài liệu tham khảo

Chanson, 1989, Heart failure responding to octreotide in patient with acromegaly, Lancet, 1, 1263, 10.1016/S0140-6736(89)92355-6 Chollet Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Gencturk, 2013, 817 Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216 Hamet, 2017, Artificial intelligence in medicine, Metabolism, 69S, S36, 10.1016/j.metabol.2017.01.011 Kanakasabapathy, 2017, An automated smartphone-based diagnostic assay for point-of-care semen analysis, Sci. Transl. Med., 9, 10.1126/scitranslmed.aai7863 Katznelson, 2014, Acromegaly: an endocrine society clinical practice guideline, J. Clin. Endocrinol. Metab., 99, 3933, 10.1210/jc.2014-2700 Kruszka, 2017, 22q11.2 deletion syndrome in diverse populations, Am. J. Med. Genet. A, 173, 879, 10.1002/ajmg.a.38199 Laurence, 2014, Acromegaly: an endocrine society clinical practice guideline, J. Clin. Endocrinol. Metab., 99 Learned-Miller, 2006, Detecting acromegaly: screening for disease with a morphable model, Med. Image Comput. Comput. Assist. Interv., 9, 495 Melmed, 2006, Medical progress: acromegaly, N. Engl. J. Med., 355, 2558, 10.1056/NEJMra062453 Melmed, 1990, N. Engl. J. Med., 322, 966, 10.1056/NEJM199004053221405 Miller, 2011, Early diagnosis of acromegaly: computers vs clinicians, Clin. Endocrinol., 75, 226, 10.1111/j.1365-2265.2011.04020.x Obermeyer, 2016, Predicting the future - big data, machine learning, and clinical medicine, N. Engl. J. Med., 375, 1216, 10.1056/NEJMp1606181 Reddy, 2010, Acromegaly, BMJ, 341, c4189, 10.1136/bmj.c4189 Ribeiro-Oliveira, 2012, The changing face of acromegaly—advances in diagnosis and treatment, Nat. Rev. Endocrinol., 8, 605, 10.1038/nrendo.2012.101 Sagonas Schneider, 2008, High prevalence of biochemical acromegaly in primary care patients with elevated IGF-1 levels, Clin. Endocrinol., 69, 432, 10.1111/j.1365-2265.2008.03221.x Schneider, 2011, A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification, J. Clin. Endocrinol. Metab., 96, 2074, 10.1210/jc.2011-0237 Seeliger, 2017, Convolutional neural network-based encoding and decoding of visual object recognition in space and time, NeuroImage Tikoo, 2016, Detection, segmentation and recognition of face and its features using neural network, J. Biosens. Bioelectron., 7 Utiger, 2000, Treatment of acromegaly, N. Engl. J. Med., 342, 1210, 10.1056/NEJM200004203421611 Xie
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EBioMedicine

Tập 27

94-102

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