Arslan Can, Yucel Nesli Ozum, Kahya Kaan, Sunal Akturk Ezgi, Germec Cakan Derya
Department of Orthodontics, Faculty of Dentistry, Yeditepe University, Istanbul 34728, Turkey.
Department of Orthodontics, Hamidiye Faculty of Dental Medicine, University of Health Sciences, Istanbul 34668, Turkey.
Diagnostics (Basel). 2024 Dec 18;14(24):2849. doi: 10.3390/diagnostics14242849.
Cleft lip and palate patients often present with unique anatomical challenges, making dental anomaly detection and numbering particularly complex. The accurate identification of teeth in these patients is crucial for effective treatment planning and long-term management. Artificial intelligence (AI) has emerged as a promising tool for enhancing diagnostic precision, yet its application in this specific patient population remains underexplored. This study aimed to evaluate the performance of an AI-based software in detecting and numbering teeth in cleft lip and palate patients. The research focused on assessing the system's sensitivity, precision, and specificity, while identifying potential limitations in specific anatomical regions and demographic groups. A total of 100 panoramic radiographs (52 males, 48 females) from patients aged 6 to 15 years were analyzed using AI software. Sensitivity, precision, and specificity were calculated, with ground truth annotations provided by four experienced orthodontists. The AI system's performance was compared across age and gender groups, with particular attention to areas prone to misidentification. The AI system demonstrated high overall sensitivity (0.98 ± 0.03) and precision (0.96 ± 0.04). No statistically significant differences were found between age groups ( > 0.05), but challenges were observed in the maxillary left region, which exhibited higher false positive and false negative rates. These findings were consistent with the prevalence of unilateral left clefts in the study population. The AI system was effective in detecting and numbering teeth in cleft lip and palate patients, but further refinement is required for improved accuracy in the cleft region, particularly on the left side. Addressing these limitations could enhance the clinical utility of AI in managing complex craniofacial cases.
唇腭裂患者常常面临独特的解剖学挑战，这使得牙齿异常检测和编号格外复杂。准确识别这些患者的牙齿对于有效的治疗计划和长期管理至关重要。人工智能（AI）已成为提高诊断精度的一种有前景的工具，但其在这一特定患者群体中的应用仍未得到充分探索。本研究旨在评估基于人工智能的软件在检测唇腭裂患者牙齿并为其编号方面的性能。该研究聚焦于评估系统的敏感性、精确性和特异性，同时识别特定解剖区域和人口统计学群体中的潜在局限性。使用人工智能软件分析了100张来自6至15岁患者的全景X光片（52名男性，48名女性）。计算了敏感性、精确性和特异性，由四名经验丰富的正畸医生提供真实标注。对人工智能系统在不同年龄和性别组中的性能进行了比较，特别关注容易误识别的区域。人工智能系统总体表现出较高的敏感性（0.98±0.03）和精确性（0.96±0.04）。年龄组之间未发现统计学上的显著差异（>0.05），但在上颌左侧区域观察到了挑战，该区域显示出较高的假阳性和假阴性率。这些发现与研究人群中左侧单侧唇腭裂的患病率一致。人工智能系统在检测唇腭裂患者牙齿并为其编号方面是有效的，但在腭裂区域，尤其是左侧，需要进一步改进以提高准确性。解决这些局限性可以增强人工智能在管理复杂颅面病例中的临床效用。
