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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
El Asri, Bouchra
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Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Computer aided detection for vertebral deformities diagnosis based on deep learning OUNASSER, Nabila; Rhanoui, Maryem; mikram, Mounia; El Asri, Bouchra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3414-3425

Abstract

The diagnosis of spinal deformities is one of the most frequent daily clinical routine. X-ray images are used to diagnose several pathologies in order to reduce harmful radiations of the patient. Spinal deformities are diagnosed essentially from vertebral shapes, orientations, and positions, so their detection and segmentation are major steps required for diagnosis. Deep learning could be applied for automatic diagnosis to detect scoliosis and its variants with a favourable performance. In this study, based on 609 spinal anterior-posterior x-ray images obtained from the public SpineWeb, we examine generative ad- versarial network (GAN) based architectures and convolutional neural network (CNN) based architectures models that are capable of automatically detecting anomalies in radiograph and achieve expert-level performances in various fields providing a solid comparative study. Most of the implemented models are apt to automatically distinguish limits between vertebrae so determining their shape with a very good visual performance. The GAN-based architecture estimates the required vertebral landmarks with an accuracy rate of 0.966, signify its capacity for automatic scoliosis assessment in a clinical setting.
Advancing medical imaging with GAN-based anomaly detection Ounasser, Nabila; Rhanoui, Maryem; Mikram, Mounia; El Asri, Bouchra
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp570-582

Abstract

Anomaly detection in medical imaging is a complex challenge, exacerbated by limited annotated data. Recent advancements in generative adversarial networks (GANs) offer potential solutions, yet their effectiveness in medical imaging remains largely uncharted. We conducted a targeted exploration of the benefits and constraints associated with GAN-based anomaly detection techniques. Our investigations encompassed experiments employing eight anomaly detection methods on three medical imaging datasets representing diverse modalities and organ/tissue types. These experiments yielded notably diverse results. The results exhibited significant variability, with metrics spanning a wide range (area under the curve (AUC): 0.475-0.991; sensitivity: 0.17-0.98; specificity: 0.14-0.97). Furthermore, we offer guidance for implementing anomaly detection models in medical imaging and anticipate pivotal avenues for future research. Results unveil varying performances, influenced by factors like dataset size, anomaly subtlety, and dispersion. Our findings provide insights into the complex landscape of anomaly detection in medical imaging, offering recommendations for future research and deployment.
A brief on artificial intelligence in medicine Ounasser, Nabila; Rhanoui, Maryem; Mikram, Mounia; El Asri, Bouchra
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp1055-1064

Abstract

This review explores the transformative impact of artificial intelligence (AI) in medicine. It discusses the benefits of AI, its core technologies, integration processes, and diverse applications. AI enhances diagnostics, personalizes treatments, and optimizes healthcare operations. Machine learning and deep learning are key AI technologies, while explainable AI ensures transparency. The review emphasizes the integration journey and highlights AI applications, from image diagnosis to telemedicine. Ethical concerns, data privacy, regulations, and algorithmic bias are challenges. The future promises continued innovation, global health equity, and responsible AI application in medicine.
Co-Authors Mikram, Mounia OUNASSER, Nabila Rhanoui, Maryem