Adeniyi S. Aderibigbe

Work place: Department of Radiology, Obafemi Awolowo University Teaching Hospital Complex, Ile-Ife, Nigeria



Research Interests: Artificial Intelligence


Dr Aderibigbe Adeniyi Sunday is an academic Radiologist and Honorary Consultant Radiologist at the Obafemi Awolowo University/ Obafemi Awolowo University Teaching Hospitals Complex Ile-Ife. He has special interests in musculoskeletal and Vascular imaging as well as artificial intelligence applications in radio diagnosis.

Author Articles
Design and Implementation of Diagnosis System for Cardiomegaly from Clinical Chest X-ray Reports

By Omolara A. Ogungbe Abimbola R. Iyanda Adeniyi S. Aderibigbe

DOI:, Pub. Date: 8 Jun. 2022

With the increasingly broadening adoption of Electronic Health Record (EHR) worldwide, there is a growing need to widen the use of EHR to support clinical decision making and research particularly in radiology. A number of studies on generation, analysis and presentation of chest x-ray reports from digital images to detect abnormalities have been well documented in the literature but studies on automatic analysis of chest x-ray reports have not been well represented. Interestingly, there is a large amount of unstructured electronic chest x-ray notes that need to be organized and processed in such a way that it can be automated for the purpose of giving urgent attention to abnormal radiographs in clinical findings to allow for quicker report analysis and decision making. This study developed a system to automate this analysis in order to prioritize findings from chest x-rays using support vector machine and Lagrange Multiplier for the constraint optimization. The classification model was implemented using Python programming language and Django framework. The developed system was evaluated based on precision, recall, f1-score, negative predictive value (NPV). Expert’s knowledge was also used as gold standard and comparison with the existing system. The result showed a precision of 96.04%, recall of 95.10%, f1-score of 95.57%, specificity of 86.21%, negative predictive value of 83.33% and an accuracy of 93.13%. The study revealed that a limited but important number of relevant attributes provided an effective and efficient model for the detection of cardiomegaly in clinical chest x-ray reports. From the evaluation result, it is evident that this system can help the clinicians to quickly prioritize findings from chest x-ray reports, thereby reducing the delay in attending to patients. Hence, the developed system could be used for the analysis of chest x-ray reports with the purpose of diagnosing the patient for cardiomegaly. Chest X-ray reports are usually textual, therefore, further studies can introduce spell checker to the system to provide higher sensitivity.

[...] Read more.
Other Articles