Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers.

OBJECTIVE The purpose of this article is to highlight best practices for writing and reviewing articles on artificial intelligence for medical image analysis. CONCLUSION Artificial intelligence is in the early phases of application to medical imaging, and patient safety demands a commitment to sound methods and avoidance of rhetorical and overly optimistic claims. Adherence to best practices should elevate the quality of articles submitted to and published by clinical journals.

[1]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[2]  Yang Wang,et al.  Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation , 2016, ISVC.

[3]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[4]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[5]  C A Gatsonis,et al.  Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. , 2003, Clinical radiology.

[6]  B. Erickson,et al.  Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[7]  C. Langlotz,et al.  Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs. , 2017, Radiology.

[8]  S. Park,et al.  Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. , 2018, Radiology.

[9]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[10]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[11]  H. Kundel,et al.  Measurement of observer agreement. , 2003, Radiology.

[12]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[13]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[14]  C. Pal,et al.  Deep Learning: A Primer for Radiologists. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[15]  N. Obuchowski Receiver operating characteristic curves and their use in radiology. , 2003, Radiology.

[16]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[17]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[18]  Phillip M. Cheng,et al.  Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks , 2018, Abdominal Radiology.

[19]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.