A review on cephalometric landmark detection techniques

Juneja, Mamta; Garg, Poojita; Kaur, Ravinder; Manocha, Palak; Prateek, .; Batra, Shivam; Singh, Pradeep; Singh, Shaswat; Jindal, Prashant

doi:10.1016/j.bspc.2021.102486

Cited by 28 publications

(14 citation statements)

References 33 publications

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“…Operator information, cephalometric landmark locations, and cephalograms are all stored on the database server [ 41 ]. The types of web-based software are framed in Table 2 [ 42 – 45 ], and studies related to AI in the cephalometric analysis were summarised in Table 3 [ 4 , 15 , 19 , 21 , 32 , 41 , 46 – 63 ].…”

Section: Discussionmentioning

confidence: 99%

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Subramanian

Chen

Almalki

et al. 2022

BioMed Research International

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Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic dispensation, machine learning, and planning. In the area of medicine and dentistry, machine learning is currently the most widely used AI application. This narrative review is aimed at giving an outline of cephalometric analysis in orthodontics using AI. Latest algorithms are developing rapidly, and computational resources are increasing, resulting in increased efficiency, accuracy, and reliability. Current techniques for completely automatic identification of cephalometric landmarks have considerably improved efficiency and growth prospects for their regular use. The primary considerations for effective orthodontic treatment are an accurate diagnosis, exceptional treatment planning, and good prognosis estimation. The main objective of the AI technique is to make dentists’ work more precise and accurate. AI is increasingly being used in the area of orthodontic treatment. It has been evidenced to be a time-saving and reliable tool in many ways. AI is a promising tool for facilitating cephalometric tracing in routine clinical practice and analyzing large databases for research purposes.

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Section: Discussionmentioning

confidence: 99%

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Subramanian

Chen

Almalki

et al. 2022

BioMed Research International

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“…Approaches in the development of cephalometric landmark detection can be classified into two main categories, Knowledge based approaches and AI based approaches [3]. The first category exploits pre-existing models to build a knowledge base for solving complex problems.…”

Section: Introductionmentioning

confidence: 99%

An Automated System for Cephalometric Soft-tissue Landmark Detection

Elaiwat

Azad

Alam

2022

Preprint

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The ability to detect cephalometric landmarks in x-ray images plays a crucial role in several medical applications, including orthodontics and maxillofacial surgery. The manual annotation of landmarks is time-consuming and prone to inaccuracy. These limitations can be overcome by building a system for automated landmark detection. Towards this goal, we propose a fully automated system for the task of soft-tissue landmark detection. The proposed method combines two different types of descriptors, Haar-like features and spatial features, to build a strong classifier using Adaboost technique. In order to evaluate the proposed method, we introduce a new dataset for soft-tissue landmark detection task along with two evaluation protocols to define detection rate. The first protocol represents the detection rate within mean radial error (MRE) while the second protocol represents the detection rate within a predefined confidence region R. Reported experiments demonstrate the superior performance of the proposed method compared to state-of-the-art.

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“…Sex determination [35][36][37][38][39][40][41][42][43] 3. Automatized 3D cephalometric landmark annotation [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] 4. Soft-tissue face prediction from skull and in reverse [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81] 5.…”

Section: Introductionmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine

Thurzo¹,

Kosnáčová²,

Kurilová³

et al. 2021

Preprint

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Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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A review on cephalometric landmark detection techniques

Cited by 28 publications

References 33 publications

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

An Automated System for Cephalometric Soft-tissue Landmark Detection

Use of Advanced Artificial Intelligence in Forensic Medicine

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