# Verification of Convolutional Neural Network Cephalometric Landmark Identification
## 1 Introduction  
The delineation and representation of human facial form has found expression throughout human existence. Ancient Egyptian and Greek cultures developed methods mathematical and otherwise, such as anthropometrics, for this purpose [1]. Until [PERSON]'s first report of X-rays in 1895 [2], which also were first used in dentistry in the same year [3], these can be summarized as attempts to quantify faces and bodies according to externally visualized cues and proportions. X-rays now allowed for visualization, albeit two-dimensional, of the internal hard tissues which support and provide the form outwardly seen. [PERSON], [PERSON] and [PERSON] codified its place in orthodontics when they independently developed cranium orienting cephalostats and methods to standardize cephalometric radiology, in 1931 [4, 5]. [PERSON] with [PERSON] [6, 7] initiated the emergence of a plethora of cephalometric analyses which continue to be used as diagnostic tools to quantify craniofacial characteristics [8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Proficiency in radiographic anatomic landmark identification has facilitated patient diagnosis and treatment evaluation and a significant researcher tool. However, the expanded use of this method also revealed its inherent limitations as initially described by [PERSON] [18], and ensued with descriptions of these according to errors due to superimposition of intervening structures, magnification of structures closer to the X-ray source andpoor observer landmark identification reliability, all of which limit the effectiveness of this tool [19; 20; 21; 22]. The advent of computed tomography (CT) in 1973, provided a one-to-one 3D radiographic image resolving the aforementioned shortcomings [23]; however, this method was appropriate for structures larger than teeth or jaws and required much higher doses of radiation than conventional clinical radiology. The introduction of the cone beam CT (CBCT) in 2001, enabled its use in dentistry. This technology now allows orthodontists to receive highly accurate representations of cranial structures, including 3D renderings, within a single diagnostic record while exposing the patient to less radiation than a conventional panoramic radiograph [24; 25]. Digitization of cephalometrics has occurred together with other medical diagnostic measures. Initially, traditional radiographs were used directly or scanned into software from which an operator could manually plot the necessary points [26; 27; 28; 29; 30]. This approach was only feasible within an institutional setting where staff could be directed and were available to do so, until technological advances bypassed this function [31]. In addition, automatic landmark recognition from the now digitally acquired image has also been evolving [32; 33; 34; 35; 36; 37]. The availability of the needed volume of pertinent digital data has catalyzed the development of various modes of machine learning and artificial intelligence (AI), which are being applied to perform autonomous landmark recognition [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. The most commonly used machine learning method of image recognition and classification are neural networks. These are currently applied in identification of objects, faces, traffic signs and to generate vision in self-driving cars, etc. [54]. Convolutional neural networks (CNNs), like neural networks, are made up of \"neurons\" with learnable weights and biases. Each neuron receives several inputs, takes a weighted sum over them, passes it through an activation function and responds with an output [55]. In this manner, CNNs are an attempt to mimic the decision-making tasks performed by our own central nervous system [56]. The objective of the present study is to evaluate the accuracy and reliability of automatic computer-generated lateral cephalometric landmark recognition by the Algoceph(r) convolutional neural network (CNN) AI system (Orca Dental, Hertzliya, Israel).