Bridging Precision and Efficiency: Ai-Driven Segmentation and Orientation Correction for Enhanced Protocol Adherence in Teledermatology

Protocol non-adherence in teledermatology, particularly during image acquisition, such as missing rulers or incorrect image orientations, leads to invalid examinations, delayed diagnoses, and increased patient burdens. This study addresses these challenges by evaluating modern neural network architectures for instance segmentation and proposing a novel orientation correction pipeline to improve adherence in two protocols: Approximation (identifying rulers/patient tags) and Panoramic (correcting body orientation errors). Using the Santa Catarina State Telemedicine System dataset (14,238 images for Approximation; 3,692 for Panoramic), models were benchmarked against previous study measuring computational efficiency and precision metrics. Results demonstrate YOLOv11's superiority, achieving state-of-the-art performance with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$86.08 \text{AP}_{75}$</tex>, reducing segmentation errors by 14 % compared to Mask R-CNN, while maintaining computational efficiency (1.3 GB VRAM, 566-699 ms latency). For panoramic protocol images, our proposed pipeline mitigated orientation errors by methodically rotating misaligned human masks, improving weighted <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex>-scores from 0.51 to 0.82 and significantly reducing misclassifications between valid and invalid poses. While Mask2Former's transformerbased design architecture exhibited higher precision, the computational demands (11.7 GB VRAM) hindered deployability in low-resource clinics. This study concludes that hybrid models like YOLOv11 optimally balance segmentation accuracy and operational efficiency, offering actionable insights for real-world clinical implementation bridging AI advancements with clinical pragmatism, improving protocol adherence to reduce invalid examinations by addressing orientation inconsistencies in teledermatology.