Evaluating a Fusion Teaching-Learning Method for Enhancing Comprehension, Retention, and Clinical Application of ENT Surgical Anatomy in MBBS Students

Traditional methods of anatomy instruction often fall short in promoting clinically relevant, long-term understanding, particularly in surgical domains such as otorhinolaryngology (ENT), which require high-level spatial reasoning and procedural competence. This study aimed to evaluate the effectiveness of an innovative, multimodal Fusion Teaching-Learning Method (TLM), conceptualised as "From Pixels to Scalpels," that integrates digital visualisation, hands-on engagement, and collaborative case-based tasks in enhancing the comprehension, retention, and clinical application of ENT surgical anatomy among MBBS students. A prospective, crossover observational study was conducted involving 60 Phase III Part I MBBS students at a tertiary teaching institution. Participants experienced both traditional and Fusion TLM interventions across two ENT topics, with outcomes assessed through pre-, post-, and two-week retention multiple-choice question (MCQ) tests, rubric-based team assessments, and structured perception surveys. Statistical analysis included paired and unpaired t-tests for knowledge comparisons and descriptive analysis for student feedback. The Fusion TLM significantly improved post-test scores (p = 0.007 and 0.004), rubric-based application scores (p = 0.003 and 0.002), and knowledge retention (p = 0.003 and 0.001) compared to traditional methods. Over 90% of students reported enhanced engagement, conceptual clarity, and confidence, with 75% affirming improved self-efficacy and clinical reasoning abilities. The "From Pixels to Scalpels" model demonstrated clear pedagogical superiority, combining cognitive, psychomotor, and affective domains to foster deeper learning and clinical relevance. Its scalability, learner-centred design, and alignment with Competency-Based Medical Education (CBME) make it a promising strategy for anatomical education in resource-constrained settings.