Matching Items (2)
Description
Traumatic injuries are the leading cause of death in children under 18, with head trauma being the leading cause of death in children below 5. A large but unknown number of traumatic injuries are non-accidental, i.e. inflicted. The lack of sensitivity and specificity required to diagnose Abusive Head Trauma (AHT) from radiological studies results in putting the children at risk of re-injury and death. Modern Deep Learning techniques can be utilized to detect Abusive Head Trauma using Computer Tomography (CT) scans. Training models using these techniques are only a part of building AI-driven Computer-Aided Diagnostic systems. There are challenges in deploying the models to make them highly available and scalable.
The thesis models the domain of Abusive Head Trauma using Deep Learning techniques and builds an AI-driven System at scale using best Software Engineering Practices. It has been done in collaboration with Phoenix Children Hospital (PCH). The thesis breaks down AHT into sub-domains of Medical Knowledge, Data Collection, Data Pre-processing, Image Generation, Image Classification, Building APIs, Containers and Kubernetes. Data Collection and Pre-processing were done at PCH with the help of trauma researchers and radiologists. Experiments are run using Deep Learning models such as DCGAN (for Image Generation), Pretrained 2D and custom 3D CNN classifiers for the classification tasks. The trained models are exposed as APIs using the Flask web framework, contained using Docker and deployed on a Kubernetes cluster.
The results are analyzed based on the accuracy of the models, the feasibility of their implementation as APIs and load testing the Kubernetes cluster. They suggest the need for Data Annotation at the Slice level for CT scans and an increase in the Data Collection process. Load Testing reveals the auto-scalability feature of the cluster to serve a high number of requests.
The thesis models the domain of Abusive Head Trauma using Deep Learning techniques and builds an AI-driven System at scale using best Software Engineering Practices. It has been done in collaboration with Phoenix Children Hospital (PCH). The thesis breaks down AHT into sub-domains of Medical Knowledge, Data Collection, Data Pre-processing, Image Generation, Image Classification, Building APIs, Containers and Kubernetes. Data Collection and Pre-processing were done at PCH with the help of trauma researchers and radiologists. Experiments are run using Deep Learning models such as DCGAN (for Image Generation), Pretrained 2D and custom 3D CNN classifiers for the classification tasks. The trained models are exposed as APIs using the Flask web framework, contained using Docker and deployed on a Kubernetes cluster.
The results are analyzed based on the accuracy of the models, the feasibility of their implementation as APIs and load testing the Kubernetes cluster. They suggest the need for Data Annotation at the Slice level for CT scans and an increase in the Data Collection process. Load Testing reveals the auto-scalability feature of the cluster to serve a high number of requests.
ContributorsVikram, Aditya (Author) / Sanchez, Javier Gonzalez (Thesis advisor) / Gaffar, Ashraf (Thesis advisor) / Findler, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Background: Pulmonary embolism is a deadly condition that is often diagnosed using a technique known as computed tomography pulmonary angiography (CTPA). CTPA reports are free-text, narrative-style forms of documentation conferring radiologist findings—both primary (regarding pulmonary embolism) and incidental. This project seeks to combine simple natural language processing (NLP) techniques, such as regular expressions and rules, to build upon and
further process output from a machine learning based named entity recognition (NER) tool for the purposes of (1) linking references to radiological images with the corresponding clinical findings and (2) extracting primary and incidental findings.
Methods: The project’s system utilized a regular expression to extract image references. All CTPA reports were first processed with NER software to obtain the text and spans of clinical findings. A heuristic was used to determine the appropriate clinical finding that should be linked with a particular image reference. Another regular expression was used to extract primary findings from NER output; the remaining findings were considered incidental. Performance was
assessed against a gold standard, which was based upon a manually annotated version of the CTPA reports used in this project.
Results: Extraction of image references achieved a 100% accuracy. Linkages between these references and exact gold standard spans of the clinical findings achieved a precision of 0.24, a recall of 0.22, and an F1 score of 0.23. Linkages with partial spans of clinical findings as determined by the gold standard achieved a precision of 0.71, a recall of 0.67, and an F1 score of 0.69. Primary and incidental finding extraction achieved a precision of 0.67, a recall of 0.80, and
an F1 score of 0.73.
Discussion: Various elements reduced system performance such as the difficulty of exactly matching the spans of clinical findings from NER output with those found in the gold standard. The heuristic linking clinical findings and image references was especially sensitive to NER false positives and false negatives due to its assumption that the appropriate clinical finding was that which was immediately prior to the image reference. Although the system did not perform as well as hoped, lessons were learned such as the need for clear research methodology and proper gold standard creation; without a proper gold standard, problem scope and system performance cannot be properly assessed. Improvements to the system include creating a more robust heuristic, sifting NER false positives, and training the NER tool used on a dataset of CTPA reports.
further process output from a machine learning based named entity recognition (NER) tool for the purposes of (1) linking references to radiological images with the corresponding clinical findings and (2) extracting primary and incidental findings.
Methods: The project’s system utilized a regular expression to extract image references. All CTPA reports were first processed with NER software to obtain the text and spans of clinical findings. A heuristic was used to determine the appropriate clinical finding that should be linked with a particular image reference. Another regular expression was used to extract primary findings from NER output; the remaining findings were considered incidental. Performance was
assessed against a gold standard, which was based upon a manually annotated version of the CTPA reports used in this project.
Results: Extraction of image references achieved a 100% accuracy. Linkages between these references and exact gold standard spans of the clinical findings achieved a precision of 0.24, a recall of 0.22, and an F1 score of 0.23. Linkages with partial spans of clinical findings as determined by the gold standard achieved a precision of 0.71, a recall of 0.67, and an F1 score of 0.69. Primary and incidental finding extraction achieved a precision of 0.67, a recall of 0.80, and
an F1 score of 0.73.
Discussion: Various elements reduced system performance such as the difficulty of exactly matching the spans of clinical findings from NER output with those found in the gold standard. The heuristic linking clinical findings and image references was especially sensitive to NER false positives and false negatives due to its assumption that the appropriate clinical finding was that which was immediately prior to the image reference. Although the system did not perform as well as hoped, lessons were learned such as the need for clear research methodology and proper gold standard creation; without a proper gold standard, problem scope and system performance cannot be properly assessed. Improvements to the system include creating a more robust heuristic, sifting NER false positives, and training the NER tool used on a dataset of CTPA reports.
ContributorsBorlongan, Matthew Bilog (Author) / Devarakonda, Murthy (Thesis director) / Murcko, Anita (Committee member) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05