Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

An evaluation of a checklist in Musculoskeletal (MSK) radiographic image interpretation when using Artificial Intelligence (AI)

Background: AI is being used increasingly in image interpretation tasks. There are challenges for its optimal use in reporting environments. Human reliance on technology and bias can cause decision errors. Trust issues exist amongst radiologists and radiographers in both over-reliance (automation bias) and reluctance in AI use for decision support. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene. Method: A checklist, to be used in image interpretation with AI assistance, was developed. Participants interpreted 20 examinations with AI assistance and then re- interpreted the 20 examinations with AI and a checklist. The MSK images were presented to radiographers as patient examinations to replicate the image interpretation task in clinical practice. Image diagnosis and confidence levels on the diagnosis provided were collected following each interpretation. The participant perception of the use of the checklist was investigated via a questionnaire.Results: Data collection and analysis are underway and will be completed at the European Congress of Radiology in Vienna, March 2023. The impact of the use of a checklist in image interpretation with AI will be evaluated. Changes in accuracy and confidence will be investigated and results will be presented. Participant feedback will be analysed to determine perceptions and impact of the checklist also. Conclusion: A novel checklist has been developed to aid the interpretation of images when using AI. The checklist has been tested for its use in assisting radiographers in MSK image interpretation when using AI.<br/

Automated Quality Assessment of Printed Objects Using Subjective and Objective Methods Based on Imaging and Machine Learning Techniques.

Estimating the perceived quality of printed patterns is a complex task as quality is subjective. A study was conducted to evaluate how accurately a machine learning method can predict human judgment about printed pattern quality. The project was executed in two phases: a subjective test to evaluate the printed pattern quality and development of the machine learning classifier-based automated objective model. In the subjective experiment, human observers ranked overall visual quality. Object quality was compared based on a normalized scoring scale. There was a high correlation between subjective evaluation ratings of objects with similar defects. Observers found the contrast of the outer edge of the printed pattern to be the best distinguishing feature for determining the quality of object. In the second phase, the contrast of the outer print pattern was extracted by flat-fielding, cropping, segmentation, unwrapping and an affine transformation. Standard deviation and root mean square (RMS) metrics of the processed outer ring were selected as feature vectors to a Support Vector Machine classifier, which was then run with optimized parameters. The final objective model had an accuracy of 83%. The RMS metric was found to be more effective for object quality identification than the standard deviation. There was no appreciable difference in using RGB data of the pattern as a whole versus using red, green and blue separately in terms of classification accuracy. Although contrast of the printed patterns was found to be an important feature, other features may improve the prediction accuracy of the model. In addition, advanced deep learning techniques and larger subjective datasets may improve the accuracy of the current objective model