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Real-time Interpretation: The next frontier in radiology AI - MedCity News


In the nine years since AlexNet spawned the age of deep learning, artificial intelligence (AI) has made significant technological progress in medical imaging, with more than 80 deep-learning algorithms approved by the U.S. FDA since 2012 for clinical applications in image detection and measurement. A 2020 survey found that more than 82% of imaging providers believe AI will improve diagnostic imaging over the next 10 years and the market for AI in medical imaging is expected to grow 10-fold in the same period. Despite this optimistic outlook, AI still falls short of widespread clinical adoption in radiology. A 2020 survey by the American College of Radiology (ACR) revealed that only about a third of radiologists use AI, mostly to enhance image detection and interpretation; of the two thirds who did not use AI, the majority said they saw no benefit to it. In fact, most radiologists would say that AI has not transformed image reading or improved their practices.

Artificial Intelligence in PET: an Industry Perspective Artificial Intelligence

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This paper provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI, and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom designed data processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment Artificial Intelligence

Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of label noise that can impact the performance. In this study, we propose novel training strategies that handle label noise from such suboptimal data. Prior label probabilities were measured on a subset of training data re-read by 4 board-certified radiologists and were used during training to increase the robustness of the training model to the label noise. Furthermore, we exploit the high comorbidity of abnormalities observed in chest radiography and incorporate this information to further reduce the impact of label noise. Additionally, anatomical knowledge is incorporated by training the system to predict lung and heart segmentation, as well as spatial knowledge labels. To deal with multiple datasets and images derived from various scanners that apply different post-processing techniques, we introduce a novel image normalization strategy. Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets. With an average AUC score of 0.880 across all abnormalities, our proposed training strategies can be used to significantly improve performance scores.

Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing Machine Learning

Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison.We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks

Separation of target anatomical structure and occlusions in chest radiographs Machine Learning

Chest radiographs are commonly performed low-cost exams for screening and diagnosis. However, radiographs are 2D representations of 3D structures causing considerable clutter impeding visual inspection and automated image analysis. Here, we propose a Fully Convolutional Network to suppress, for a specific task, undesired visual structure from radiographs while retaining the relevant image information such as lung-parenchyma. The proposed algorithm creates reconstructed radiographs and ground-truth data from high resolution CT-scans. Results show that removing visual variation that is irrelevant for a classification task improves the performance of a classifier when only limited training data are available. This is particularly relevant because a low number of ground-truth cases is common in medical imaging.

Statistical Exploration of Relationships Between Routine and Agnostic Features Towards Interpretable Risk Characterization Machine Learning

As is typical in other fields of application of high throughput systems, radiology is faced with the challenge of interpreting increasingly sophisticated predictive models such as those derived from radiomics analyses. Interpretation may be guided by the learning output from machine learning models, which may however vary greatly with each technique. Whatever this output model, it will raise some essential questions. How do we interpret the prognostic model for clinical implementation? How can we identify potential information structures within sets of radiomic features, in order to create clinically interpretable models? And how can we recombine or exploit potential relationships between features towards improved interpretability? A number of statistical techniques are explored to assess (possibly nonlinear) relationships between radiological features from different angles.

Secure and Robust Machine Learning for Healthcare: A Survey Machine Learning

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

What has Artificial Intelligence done for radiology lately in 21st Century?


Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Artificial Intelligence (AI), sometimes called machine intelligence, is Computer systems theory and engineering capable of performing tasks that typically require human intelligence, such as visual processing, speech recognition, decision-making, and language translation. To further expand this concept of AI in the scope of radiology results in "a computer science unit dealing with the processing, reconstruction, analysis and/or analysis of medical images by simulating intelligent human behavior in computers." Radiology, also defined as diagnostic imaging, is a series of different tests that take images of different parts of the body.

A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence


To delineate image data curation needs and describe a locally designed graphical user interface (GUI) to aid radiologists in image annotation for artificial intelligence (AI) applications in medical imaging. GUI components support image analysis toolboxes, picture archiving and communication system integration, third-party applications, processing of scripting languages, and integration of deep learning libraries. For clinical AI applications, GUI components included two-dimensional segmentation and classification; three-dimensional segmentation and quantification; and three-dimensional segmentation, quantification, and classification. To assess radiologist engagement and performance efficiency associated with GUI-related capabilities, image annotation rate (studies per day) and speed (minutes per case) were evaluated in two clinical scenarios of varying complexity: hip fracture detection and coronary atherosclerotic plaque demarcation and stenosis grading. For hip fracture, 1050 radiographs were annotated over 7 days (150 studies per day; median speed: 10 seconds per study [interquartile range, 3–21 seconds per study]).