Artificial intelligence system spots lung cancer before radiologists

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CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.


Artificial intelligence system spots lung cancer before radiologists

#artificialintelligence

CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.


Highly accurate model for prediction of lung nodule malignancy with CT scans

arXiv.org Machine Learning

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX.


Deep learning helps radiologists detect lung cancer on chest X-rays – Physics World

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Chest radiography is the most common imaging exam used for lung cancer screening. However, the size, density and location of lung lesions make their detection on chest X-rays challenging. Recently, machine-learning methods have been developed to help improve diagnostic accuracy, with deep convolutional neural networks (DCNNs), showing promise for chest radiograph interpretation. A study from four medical centres on three continents has now demonstrated that DCNN software can improve radiologists' detection of malignant lung cancers on chest X-rays (Radiology 10.1148/radiol.2019182465). "The average sensitivity of radiologists was improved by 5.2% when they re-reviewed X-rays with the deep-learning software," says Byoung Wook Choi from Yonsei University College of Medicine in Seoul, Korea.


Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

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Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning–based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Investigators at four medical centers retrospectively identified 600 lung cancer–containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning–based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software.