Phd in ADVANCED SURGICAL TECHNOLOGIES

Thesis title: ARTIFICIAL INTELLIGENCE IN PREDICTING TREATMENT AND FOLLOW UP OF THYROID DISORDERS COMPARED WITH FINE NEEDLE ASPIRATION

Abstract Introduction: Thyroid nodules categorized as Bethesda III and IV remain diagnostically challenging due to indeterminate cytology, often leading to unnecessary surgeries or delayed interventions. Fine Needle Aspiration Biopsy (FNAB) indeterminate in these cases, prompting the need for alternative diagnostic tools. Aim: To evaluate the diagnostic accuracy and clinical utility of an Artificial Intelligence (AI) classification system for thyroid nodules with indeterminate cytology (Bethesda III and IV), and compare its performance with histological outcomes and clinical decisions. Methods: This prospective study was conducted at the Service of Endocrinology and Surgery, University Hospital Center “Mother Teresa” in Tirana and Memorial Hospital in Fier, Albania, between January 2022 and June 2024. A total of 138 patients with Bethesda III and IV nodules were included. An AI diagnostic model classified each nodule as benign or malignant based on imaging and cytological features. These classifications were compared with final histological diagnoses and corresponding treatment decisions. Results: Across all 138 patients, the AI classifier showed balanced accuracy (~87%) with strong rule-out performance (NPV ~90%). FNAB was excellent where evaluable (II/V/VI)—near-perfect specificity—but doesn’t cover indeterminate nodules. In the Bethesda III–IV subgroup (n=53), AI maintained solid accuracy (~87%), with high NPV (95%) and moderate PPV (62%). Management aligned with AI labels: AI-benign was usually observed (60%), AI-malignant usually operated (85%), with a significant association (p=0.009; OR≈8). In multivariable analysis, an AI-malignant label independently predicted surgery (OR 7.06, 95% CI 1.18–42.42; p=0.032) after adjusting for age, sex, laterality, and nodule structure. Mixed vs cystic structure was associated with lower odds of surgery (OR 0.18, 95% CI 0.03–0.97; p=0.046). Conclusions: AI models offer a accurate, non-invasive solution for managing indeterminate thyroid nodules. Their ability to classify all cases—especially where 10 FNAB fails—places AI as a transformative tool in thyroid cancer diagnostics and surgical decision-making.