Assessment of image quality and diagnostic accuracy in digital radiography using AI-based enhancement and artifact reduction techniques

Project Abstract

This study investigates the impact of AI-based enhancement and artifact reduction techniques on image quality and diagnostic accuracy in digital radiography, addressing the persistent challenge of balancing noise, contrast, and spatial resolution while maintaining patient safety. A multi-method approach was employed, combining quantitative image quality metrics, observer performance evaluation, and AI-driven processing pipelines. A dataset comprising standardized digital radiographs across common anatomical regions was annotated by radiologists to establish ground truth for diagnostic tasks such as fracture detection, calcification assessment, and lesion characterization. Baseline images were processed with conventional enhancement methods, after which several AI-based algorithms were applied, including deep learning-based denoising, super-resolution, and artifact suppression models. The AI models were trained on paired datasets created by simulating varying exposure parameters and introducing common radiographic artifacts (motion blur, metal streaks, under- and over-exposure) to evaluate robustness and generalizability. Image quality was assessed using objective metrics like signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), Modulation Transfer Function (MTF), and Noise Power Spectrum (NPS), alongside perceptual quality scores from radiologists employing standardized scoring systems. Diagnostic accuracy was measured through receiver operating characteristic (ROC) analysis, sensitivity, specificity, and area under the curve (AUC) for predefined clinical tasks. The study also examined dose efficiency by comparing image quality improvements relative to exposure parameters, aiming to achieve diagnostic equivalence or superiority at lower or equivalent dose levels. Statistical analysis included repeated-measures ANOVA and mixed-effects models to account for reader variability and region-specific differences. Key findings indicate that AI-based enhancement substantially improves suppression of quantum mottle and structural noise while preserving edges, leading to notable gains in CNR without introducing clinically significant artifacts. Artifact reduction algorithms demonstrated improved visualization of subtle fracture lines and calcifications, particularly in borderline cases where conventional processing failed to provide diagnostic clarity. Radiologist performance improved in terms of lesion detectability and characterization accuracy, with ROC AUC increases of clinically meaningful magnitude across multiple anatomical sites. Importantly, the integrated pipeline maintained anatomical fidelity, reducing the risk of over-smoothing and artifact removal that could obscure critical findings. The research also identifies practical considerations for clinical integration, including model explainability, workflow compatibility with PACS/RIS, and the need for rigorous external validation across diverse patient populations and imaging hardware. Limitations include potential bias introduced by training data composition, the necessity of real-world prospective trials, and challenges in harmonizing AI outputs with established radiographic protocols. The study contributes a framework for evaluating AI-assisted radiography that couples objective image quality metrics with clinically relevant diagnostic outcomes, providing evidence to guide adoption strategies and regulatory oversight. Recommendations include standardized benchmarking datasets, transparent reporting of AI performance, and continuous monitoring to ensure sustained diagnostic reliability in routine practice.

Project Overview

What This Project Is About

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A straightforward look at how X-ray images in digital form can be improved using smart computer tools, and how these improvements affect doctors’ ability to read them accurately. The project combines simple testing of image clarity with basic checks of diagnostic decisions made from the images.

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The Problem It Addresses

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Digital radiographs can be noisy or blurry, making it harder to spot important details. This project explores AI-based enhancements that clean up images and reduce unwanted marks (artifacts) without changing the true medical information, aiming to reduce misreads and improve patient care.

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Objectives of the Project

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1. Identify common image quality issues in digital radiography.
2. Evaluate AI-based enhancement methods for clarity and detail preservation.
3. Assess how artifact reduction affects diagnostic confidence.
4. Compare reader performance on original vs. processed images.
5. Provide practical guidance on implementing safe enhancements in clinics.

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What You Will Do Step by Step

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1) Review basic radiography quality factors and artifacts. 2) Collect a sample set of digital X-ray images. 3) Apply AI-based enhancement and artifact reduction tools. 4) Have radiologists or trainees rate image quality and diagnostic confidence. 5) Analyze whether enhancements improve readings without introducing errors. 6) Summarize findings and draft practical recommendations.

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Expected Outcome

Clearer images with maintained or improved diagnostic accuracy, plus a set of guidelines for safe use of enhancement tools in everyday radiography practice.