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Artificial intelligence system spots lung cancer before radiologists


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.

Using artificial intelligence to predict risk of thyroid cancer on ultrasound


Thyroid nodules are small lumps that form within the thyroid gland and are quite common in the general population, with a prevalence as high as 67%. The great majority of thyroid nodules are not cancerous and cause no symptoms. However, there are currently limited guidelines on what to do with a nodule when the risk of cancer is uncertain. A new study from The Sidney Kimmel Cancer Center--Jefferson Health investigates whether a non-invasive method of ultrasound imaging, combined with a Google-platform machine-learning algorithm, could be used as a rapid and inexpensive first screen for thyroid cancer. "Currently, ultrasounds can tell us if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy or not," says Elizabeth Cottril, MD, an otolaryngologist at Thomas Jefferson University, and clinical leader of the study.

Diagnostic Classification Of Lung Nodules Using 3D Neural Networks Machine Learning

Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designing effective treatment and making financial and care plans. In this paper, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images, which aims to learn a direct mapping from 3D images to class labels. To achieve this goal, four two-pathway Convolutional Neural Networks (CNN) are proposed, including a basic 3D CNN, a novel multi-output network, a 3D DenseNet, and an augmented 3D DenseNet with multi-outputs. These four networks are evaluated on the public LIDC-IDRI dataset and outperform most existing methods. In particular, the 3D multi-output DenseNet (MoDenseNet) achieves the state-of-the-art classification accuracy on the task of end-to-end lung nodule diagnosis. In addition, the networks pretrained on the LIDC-IDRI dataset can be further extended to handle smaller datasets using transfer learning. This is demonstrated on our dataset with encouraging prediction accuracy in lung nodule classification.

Artificial Intelligence to Predict Thyroid Cancer Risk in Ultrasound


Using Artificial Intelligence (AI) with thyroid ultrasound offers a quick and non-invasive approach to thyroid cancer screening, says a new study. The study, published in the journal PLOS Pathogens, suggests that automated machine learning shows promise as an additional diagnostic tool that could improve the efficiency of thyroid cancer diagnosis. "Machine learning is a low-cost and efficient tool that could help physicians arrive at a quicker decision as to how to approach an indeterminate nodule," said the study's lead author John Eisenbrey from Thomas Jefferson University in the US. According to the researchers, at present ultrasounds can tell if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy, but fine-needle biopsies only act as a peephole, they don't reveal the whole picture. As a result, some biopsies return inconclusive results as to whether the nodule is malignant, or cancerous in other words.

Deep learning assists in detecting malignant lung cancers


Radiologists assisted by deep-learning based software were better able to detect malignant lung cancers on chest X-rays, according to research published in the journal Radiology. "The average sensitivity of radiologists was improved by 5.2% when they re-reviewed X-rays with the deep-learning software," said Byoung Wook Choi, M.D., Ph.D., professor at Yonsei University College of Medicine, and cardiothoracic radiologist in the Department of Radiology in the Yonsei University Health System in Seoul, Korea. "At the same time, the number of false-positive findings per image was reduced." Dr. Choi said the characteristics of lung lesions including size, density, and location make the detection of lung nodules on chest X-rays more challenging. However, machine learning methods, including the implementation of deep convolutional neural networks (DCNN), have helped to improve detection.