Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen.

Artificial intelligence (AI), the theory and development of computer systems able to perform tasks that normally require human intelligence, is creeping into almost every facet of modern life. Familiar examples include computer chess games, speech recognition, intelligent routing in content delivery networks, and autonomous driving cars. In the financial sector, AI is routinely used for fraud detection, algorithmic trading, and chatbots (ie, computer programs that appear to conduct conversations via auditory or textual methods, such as with online virtual assistants). Health care has been slower to adopt AI but the pace of implementation is accelerating at an impressive rate. In 2014, the acquisition of AI startups in health care was about $600 million; in 2021, it is anticipated to be $6.6 billion or a 40% compound annual growth rate.1 One reason health care is ripe for AI is “big data”: the health care industry has rich data sets that are ideal for AI given the requirement for large test sets of data with which the computer can “learn. ” Most computer modeling enhancements in health care, particularly in the image analysis field, have focused on feature engineering, essentially asking a computer to evaluate explicit features specified by experts. This permits the algorithms to detect abnormalities or predict specified lesions. In contrast, deep learning is a form of AI that includes machine learning techniques that perform iterative optimization strategies that are based on pixel-by-pixel evaluation of the data from images.2 The promise of AI in health care is the delivery of improved quality and safety of care and the potential to democratize expertise. For example, in a study by Esteva et al,3 the authors compared the ability of a deep convolutional neural network (CNN) to discriminate the most common skin cancers including malignant melanoma. They compared and demonstrated at least equivalence in the performance of their algorithm against 21 board-certified dermatologists in evaluating biopsy-proven clinical images. In this example, AI was used to discriminate whether skin lesions were malignant. The authors suggested that mobile devices, like smartphones, could be deployed with similar algorithms, permitting potentially low-cost universal access to vital diagnostic care anywhere in the world. In another study, Gulshan et al4 applied a deep CNN approach to a test set of more than 128 000 retinal fundus images from adult patients with diabetes to identify referable diabetic retinopathy. The algorithm developed had very high sensitivity and specificity for detecting referable diabetic retinopathy and macular edema.4 This study established a clear path toward use of AI not to replace physicians, but rather to perform simple, cost-effective, and widely available examinations and analyses that could help identify at-risk patients who require referral for specialty care while reassuring other patients that potential retinal manifestations of their diabetes are not present or are stable. Radiology, having converted to digital images more than 25 years ago, is well-positioned to deploy AI for diagnostics. Several studies have shown considerable opportunity to support radiologists in evaluating a variety of scan types including mammography for breast lesions, computed tomographic scans for pulmonary nodules and infections, and magnetic resonance images for brain tumors including the molecular classification of brain tumors.5-9 In contrast to radiology, pathology has been late to adopt digital imaging and thus computer-assisted diagnostic technologies. In part, this is the result of practical and financial obstacles. With conversion to digital images, radiology eliminated film, chemicals, developers, and storage of the films. Radiology departments also solved problems related to loss of films and transport of films to where they are needed, for example, in operating rooms, emergency departments, and intensive care units. Unforeseen at the time, although anticipated by some, was the inherent value within these images for greater learning using computers to improve the quality, safety, and efficiency of radiologists. Many, if not most, of the practical benefits realized by radiology would not be achieved with pathology digitization. An anatomic pathology workflow that includes digital pathology will not reduce or remove the need to produce and ultimately store glass slides of pathology specimens. Instead of any reductions, digital pathology will require additional workflows, personnel, equipment, and importantly storage of data (it is estimated that digital pathology images are at least 10 times larger files than radiology images), all on top of an already financially and operationally stressed health care system. Certainly, the adoption of digital pathology will bring some advantages, particularly in areas such as rapid teleconsultations with experts and in quality and safety. Nonetheless, Author Video Interview and JAMA Report Video