_applied sciences_  
_Article_  
Precision and Accuracy Assessment of Cephalometric Analyses  
Performed by Deep Learning Artificial Intelligence with and without Human Augmentation  
**[PERSON] 1**, **[PERSON] 1**, **[PERSON] 2**, **[PERSON] 1**, **[PERSON] 3**, **[PERSON] 3**, **[PERSON] 4** and **[PERSON] 1,*4**  
Additionally, reliability varies depending on the landmark being identified. The landmarks condylion, gnathion, orbitale, and anterior nasal spine tended to have the lowest inter-examiner reliability among experienced dental radiologists and orthodontists [4]. Computing technology demonstrates the potential to automate this process through artificial intelligence (AI). AI refers to the development of computer systems that can perform tasks that would typically require human intelligence. These tasks include but are not limited to: learning, reasoning, problem-solving, perception, and language understanding. AI systems often employ algorithms and models to process and interpret data, enabling them to make informed decisions or predictions. In dentistry, AI has been used in a variety of applications, such as to assist in the diagnosis of vertical root fractures, diagnosis of proximal caries and localization of root canals [5]. One area in which AI has been used in orthodontics is automatic lateral cephalometric landmark identification. Pioneered by [PERSON] in 1984, preliminary attempts to locate landmarks using AI were limited to easily identifiable landmarks such as sella and menton [6]. Since [PERSON]'s attempt to automate landmark identification, several AI methods have been developed to increase accuracy and precision. Success rates of these methods, defined as a deviation of less than 2 mm between the distance measured from the AI-marked landmark and the expert-marked landmark, have varied from 35 to 84.7% depending on the methodology [5]. Convolutional neural networks (CNNs), a type of deep-learning algorithm, have demonstrated some of the highest accuracy in automatic lateral cephalometric landmark identification [7]. The CNN You-Only-Look-Once version 3 (YOLOv3) is a frontrunner in this aspect [8; 9]. More recent studies have attempted to improve upon the precision and accuracy of the YOLOv3 algorithm, albeit with limited success. [PERSON] used a customized CNN using PyTorch software (Facebook Artificial Intelligence Research; Facebook, Menlo Park, CA, USA) and found a mean radial error of about 1 mm, averaged among 37 landmarks [10]. However, the mean radial errors varied significantly, ranging from 0.5 mm for pronasale to 2 mm forogonion [10]. [PERSON] utilized a CNN using CephX software and found significant differences between AI and manual measurements for saddle angle, U1-NA (mm), and L1-NB (mm) measurements [11]. Manual adjustment of landmarks identified by AI was recommended for further improvement in accuracy [11]. A systematic review conducted to evaluate the accuracy of automatic cephalometric landmark identification concluded that most CNNs demonstrated accuracy that matched the accuracy of an experienced orthodontist for many measurements [12]. In addition to two-dimensional landmark identification, studies have been conducted to evaluate the accuracy of AI in landmark identification in three dimensions. [PERSON] evaluated the accuracy of cephalometric landmark identification with AI on lateral cephalometric radiographs generated from 3D cone-beam computed tomography (CBCT) images. It was discovered that 15 of 23 cephalometric measurements were within the clinically acceptable range, or within 2 mm or degrees from the gold standard [13]. A meta-analysis by [PERSON] evaluated the accuracy of 3D landmark identification on CBCT images using only recent studies (from 2020 to 2022). As technology advanced, the mean error improved by over 2 mm over the 2-year period [14]. Despite the improvements, AI has fared better in 2D landmark identification compared to 3D, likely due to smaller sample sizes and increased computational complexity in CBCT studies [12].