A Seven-Component Anatomy-Guided Literature Review Framework for Chronic Respiratory Disease and Medical Imaging

Most computational studies in medical imaging are conducted by researchers with IT or engineering backgrounds, who may lack the clinical and anatomical expertise of medical experts. As a result, important anatomical details, clinically relevant features, and medical context are often underrepresented. This highlights the need for anatomy-guided literature reviews that integrate clinical insight with technical requirements to support the development of more generalizable and clinically meaningful methods. To address this gap, we propose a novel seven-component anatomy-guided literature review framework that links anatomical and clinical relevance with the technical aspects required to develop robust computational methods (3D) tailored to specific diseases and imaging modalities for the extraction of clinically important anatomical features. We apply the framework to Chronic Respiratory Diseases (CRDs), which cause progressive and irreversible lung damage and are a major contributor to respiratory failure and mortality in adults. Importantly, many adult CRDs are recognized to originate early in life during critical phases of lung growth and development in children. Despite this, most computational studies related to CRDs are dominated by adult cohorts, with limited focus on pediatric populations, particularly Indigenous children. The framework is demonstrated using key clinically relevant features of adult bronchiectasis (airway dilatation) and pediatric bronchiectasis (lower-lobe airway dilatation), which is highly prevalent among indigenous children. The framework integrates 3 core computational methods (airway tree extraction, artery-vein separation and extraction, and lobar and segmental localization), approx. 10 key technical aspects (branch detection, tree-length detection, leakage control, completeness and connectivity, mean-surface distance, etc.), and literature from the past decade (7 sources, approx. 20 keywords, approx. 52 publications reviewed, and 15 selected). This process enabled the identification of key limitations and 30 overall research gaps, anatomically and clinically grounded across the selected CRDs and features. Overall, the proposed framework provides a novel way for developing robust, clinically meaningful computational methods applicable to children and anatomically diverse populations across CRDs and other diseases.