The reliability of cephalometric tracing using AI

Introduction : L'objectif de cette étude est de comparer la différence entre l'analyse céphalométrique manuelle et l'analyse automatisée par l’intelligence artificielle afin de confirmer la fiabilité de cette dernière. Notre hypothèse de recherche est que la technique manuelle est la plus fiable des deux méthodes. Méthode : Un total de 99 radiographies céphalométriques latérales sont recueillies. Des tracés par technique manuelle (MT) et par localisation automatisée par intelligence artificielle (AI) sont réalisés pour toutes les radiographies. La localisation de 29 points céphalométriques couramment utilisés est comparée entre les deux groupes. L'erreur radiale moyenne (MRE) et un taux de détection réussie (SDR) de 2 mm sont utilisés pour comparer les deux groupes. Le logiciel AudaxCeph version 6.2.57.4225 est utilisé pour l'analyse manuelle et l'analyse AI. Résultats : Le MRE et SDR pour le test de fiabilité inter-examinateur sont respectivement de 0,87 ± 0,61mm et 95%. Pour la comparaison entre la technique manuelle MT et le repérage par intelligence artificielle AI, le MRE et SDR pour tous les repères sont respectivement de 1,48 ± 1,42 mm et 78 %. Lorsque les repères dentaires sont exclus, le MRE diminue à 1,33 ± 1,39 mm et le SDR augmente à 84 %. Lorsque seuls les repères des tissus durs sont inclus (excluant les points des tissus mous et dentaires), le MRE diminue encore à 1,25 ± 1,09 mm et le SDR augmente à 85 %. Lorsque seuls les points de repère des tissus mous sont inclus, le MRE augmente à 1,68 ± 1,89 mm et le SDR diminue à 78 %. Conclusion: La performance du logiciel est similaire à celles précédemment rapportée dans la littérature pour des logiciels utilisant un cadre de modélisation similaire. Nos résultats révèlent que le repérage manuel a donné lieu à une plus grande précision. Le logiciel a obtenu de très bons résultats pour les points de tissus durs, mais sa précision a diminué pour les tissus mous et dentaires. Nous concluons que cette technologie est très prometteuse pour une application en milieu clinique sous la supervision du docteur.Introduction: The objective of this study is to compare the difference between manual cephalometric analysis and automatic analysis by artificial intelligence to confirm the reliability of the latter. Our research hypothesis is that the manual technique is the most reliable of the methods and is still considered the gold standard. Method: A total of 99 lateral cephalometric radiographs were collected in this study. Manual technique (MT) and automatic localization by artificial intelligence (AI) tracings were performed for all radiographs. The localization of 29 commonly used landmarks were compared between both groups. Mean radial error (MRE) and a successful detection rate (SDR) of 2mm were used to compare both groups. AudaxCeph software version 6.2.57.4225 (Audax d.o.o., Ljubljana, Slovenia) was used for both manual and AI analysis. Results: The MRE and SDR for the inter-examinator reliability test were 0.87 ± 0.61mm and 95% respectively. For the comparison between the manual technique MT and landmarking with artificial intelligence AI, the MRE and SDR for all landmarks were 1.48 ± 1.42mm and 78% respectively. When dental landmarks are excluded, the MRE decreases to 1.33 ± 1.39mm and the SDR increases to 84%. When only hard tissue landmarks are included (excluding soft tissue and dental points) the MRE decreases further to 1.25 ± 1.09mm and the SDR increases to 85%. When only soft tissue landmarks are included the MRE increases to 1.68 ± 1.89mm and the SDR decreases to 78%. Conclusion: The software performed similarly to what was previously reported in literature for software that use analogous modeling framework. Comparing the software’s landmarking to manual landmarking our results reveal that the manual landmarking resulted in higher accuracy. The software operated very well for hard tissue points, but its accuracy went down for soft and dental tissue. Our conclusion is this technology shows great promise for application in clinical settings under the doctor’s supervision

'Aariz: A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification

The accurate identification and precise localization of cephalometric landmarks enable the classification and quantification of anatomical abnormalities. The traditional way of marking cephalometric landmarks on lateral cephalograms is a monotonous and time-consuming job. Endeavours to develop automated landmark detection systems have persistently been made, however, they are inadequate for orthodontic applications due to unavailability of a reliable dataset. We proposed a new state-of-the-art dataset to facilitate the development of robust AI solutions for quantitative morphometric analysis. The dataset includes 1000 lateral cephalometric radiographs (LCRs) obtained from 7 different radiographic imaging devices with varying resolutions, making it the most diverse and comprehensive cephalometric dataset to date. The clinical experts of our team meticulously annotated each radiograph with 29 cephalometric landmarks, including the most significant soft tissue landmarks ever marked in any publicly available dataset. Additionally, our experts also labelled the cervical vertebral maturation (CVM) stage of the patient in a radiograph, making this dataset the first standard resource for CVM classification. We believe that this dataset will be instrumental in the development of reliable automated landmark detection frameworks for use in orthodontics and beyond

Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation

An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses. The proposed frame-work is based on 2-stage u-net, regressing the multi-channel heatmaps for land-mark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, the Expansive Exploration strategy improves robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated our framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, our framework achieves state-of-the-art results

Deep learning for cephalometric landmark detection: systematic review and meta-analysis

Objectives: Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods: Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). Data: From 321 identified records, 19 studies (published 2017-2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7-93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (-0.581; 95 CI: -1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). Conclusions: DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. Clinical significance: Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective

양악 악교정수술과 수술 교정치료를 받은 골격성 III급 부정교합 환자의 측모 두부계측방사선사진 영상에서 인공지능을 이용한 경조직 계측점 식별 시 평균 오차의 변화 양상

학위논문(박사) -- 서울대학교대학원 : 치과대학 치의과학과, 2022. 8. 백승학.Objective: Recently, auto digitization of hard tissue landmarks on lateral cephalograms (Lat-cephs) has reported, with regard to artificial intelligence models using cascade convolutional neural network (CNN). The aim of this study was to investigate the pattern of accuracy change in artificial intelligence (AI)-assisted hard tissue landmark identification in serial Lat-cephs of Class III patients who underwent two-jaw orthognathic surgery and orthodontic treatment using a cascade CNN algorithm. Materials and Methods: A total of 3,188 Lat-cephs of 797 Class III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n=23 per group) for landmark identification using a cascade CNN model. Each Class III patient in the test set had four Lat-cephs: initial (T0), pre-surgery [T1, presence of orthodontic brackets (OBs)], post-surgery [T2, presence of OBs and surgical plates and screws (SPS)], and debonding [T3, presence of SPS and fixed retainers (FR)]. After mean errors of 20 hard tissue landmarks between human gold standard and the cascade CNN model were calculated, statistical analysis was performed. Results: Results are as follows. (1) The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). (2) In comparison of two time-points [(T0, T1) vs. (T2, T3)], ANS, A point, and B point showed an increase in error (P<0.01; P<0.05; P<0.01), while distal contact point of the maxillary first molar (Mx6D) and distal contact point of the mandibular first molar (Md6D) showed a decrease in error (P<0.01; P<0.01). (3) No difference in errors existed at B point, Pogonion, Menton, crown tip of the mandibular central incisor (Md1C), and root apex of the mandibular central incisor (Md1R) between the genioplasty and non-genioplasty groups. Conclusion: The cascade CNN model can be used for auto-digitization of hard tissue landmark in serial Lat-cephs including initial, pre-and post-surgery, and debonding time points despite presence of OB, SPS, FR, genioplasty, and bone remodeling.연구목적: 최근 직렬 합성곱 신경망 (cascade TJneural network) 인공지능 모델을 사용하여 측모두부계측방사선사진 영상에서 경조직 계측점을 자동 식별(auto-digitization)하는 연구들이 발표되고 있다. 본 연구의 목적은 양악 악교정수술과 술전 및 술후 교정치료를 받은 환자들의 연속적인 측모두부계측방사선사진 영상에서 경조직 계측점을 자동 식별하는 정확도가 촬영 시점에 따라 어떠한 양상으로 변화하는 지 평가하는 것이었다. 연구재료 및 방법: 연구 대상은 양악 악교정 수술을 받은 골격성 III급 부정교합 환자 797명의 측모두부계측방사선사진 영상 3,188장이었다. 751명으로부터 확보한 3,004장의 영상을 training set과 validation set으로 활용하였고, 46명 환자 [이부성형술(genioplasty) 시행 군 (n=23), 이부성형술(genioplasty) 비시행군 (n=23)]로부터 확보한 184장의 영상을 test set으로 하였다. Test set은 초진(T0), 교정용 브라켓 영상이 포함된 술전(T1), 수술용 고정판과 나사 (surgical plate and screw) 영상이 나타난 술후(T2), 수술용 고정판과 나사 및 고정식 유지장치가 영상으로 보이는 종료(T3)의 4가지 시점별로 촬영된 측모두부계측방사선사진 영상으로 구성되었다. 인공지능과 치과교정과 전문의1인(human gold standard)이 각각 20개의 경조직 계측점을 digitization 한 후, 이에 따른 오차를 통계적으로 검증하였다. 연구 결과: 그 결과는 다음과 같았다. (1) 전체 평균 오차는 1.17 mm, T0의 평균 오차는 1.20 mm, T1의 평균 오차는 1.14 mm, T2의 평균 오차는 1.18 mm, T3의 평균 오차는 1.15 mm로 나타났다. (2) 술전(T0, T1)과 술후(T2, T3)의 비교에서는, ANS, A point, B point 의 오차가 통계학적으로는 증가하였으나 임상적으로는 유의미하지 않았고 (P<0.01; P<0.05; P<0.01), Mx6D와 Md6D는 오차가 감소하였다 (P<0.01; P<0.01). (3) genioplasty 실행군과 genioplasty 비실행군 간의 비교에서 B point, Pogonion, Menton, Md1C, Md1R의 오차는 통계적인 차이가 나타나지 않았다. 결론: 직렬 인공지능 합성곱 신경망 모델은 교정 브라켓, 수술용 고정판과 나사, 고정식 유지장치, 이부성형술 그리고 수술 후 골 개조에도 불구하고, 초진, 술전 교정 치료, 술후 교정치료, 종료 시 촬영된 측모두부계측방사선사진 영상에서 경조직 계측점들을 자동식별 하는데 사용될 수 있다는 결론을 도출하였다.I. INTRODUCTION 1 II. REVIEW OF LITERATURE 3 III. MATERIALS AND METHODS 10 IV. RESULTS 14 V. DISCUSSION 17 VI. CONCLUSIONS 23 REFERENCES 25 Tables 28 Figures 36 국문초록 42박

Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages

In this paper we perform an empirical evaluation of variants of deep learning methods to automatically localize anatomical landmarks in bioimages of fishes acquired using different imaging modalities (microscopy and radiography). We compare two methodologies namely heatmap based regression and multivariate direct regression, and evaluate them in combination with several Convolutional Neural Network (CNN) architectures. Heatmap based regression approaches employ Gaussian or Exponential heatmap generation functions combined with CNNs to output the heatmaps corresponding to landmark locations whereas direct regression approaches output directly the (x, y) coordinates corresponding to landmark locations. In our experiments, we use two microscopy datasets of Zebrafish and Medaka fish and one radiography dataset of gilthead Seabream. On our three datasets, the heatmap approach with Exponential function and U-Net architecture performs better. Datasets and open-source code for training and prediction are made available to ease future landmark detection research and bioimaging applications

두부계측방사선 사진 계측점 자동 식별의 최신 기계 학습 알고리즘 간 정확도 및 연산 성능 비교 연구 – YOLOv3 vs SSD

학위논문(박사)--서울대학교 대학원 :치의학대학원 치의학과,2019. 8. 이신재.Introduction: The purpose of this study was to compare two of the latest deep learning algorithms for automatic identification of cephalometric landmarks in their accuracy and computational efficiency. This study uses two different algorithms for automated cephalometric landmark identification with an extended number of landmarks: 1) You-Only-Look-Once version 3 (YOLOv3) based method with modification, and 2) the Single Shot Detector (SSD) based method. Materials and methods: A total of 1,028 cephalometric radiographic images were selected as learning data that trained YOLOv3 and SSD methods. The number of target labelling was 80 landmarks. After the deep learning process, the algorithms were tested using a new test data set comprised of 283 images. The accuracy was determined by measuring the mean point-to-point error, success detection rate (SDR), and visualized by drawing 2-dimensional scattergrams. Computational time of both algorithms were also recorded. Results: YOLOv3 algorithm outperformed SSD in accuracy for 38/80 landmarks. The other 42/80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range, but also a more isotropic tendency. Mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature. Conclusions: Between the two algorithms applied, YOLOv3 seems to be promising as a fully automated cephalometric landmark identification system for use in clinical practice.연구 목적: 본 연구의 목적은 두부계측방사선 사진 계측점 자동 식별에 있어, 최근 개발된 두 가지 딥 러닝 알고리즘의 정확도와 연산 성능을 비교하는 것이다. 본 연구에서는 다음 두 가지의 알고리즘을 계측점 자동 식별에 적용하였다. 1) You-Only-Look-Once version 3 (YOLOv3) 및 2) the Single Shot Detector (SSD). 재료 및 방법: 총 1,028 개의 두부계측방사선 사진 영상이 YOLOv3 와 SSD방식의 학습 데이터로 사용되었다. 대상 계측점은 80개였다. 학습 과정을 거친 후, 각각의 알고리즘을 새로운 283 개의 테스트 영상에서 비교 분석하였다. 정확도는 1) 평균적인 point-to-point error, 2) success detection rate (SDR), 그리고 3) 2차원 평면에서 시각화한 scattergram 을 기반으로 평가했다. 각각의 알고리즘의 평균 연산 시간 역시 기록하였다. 결과: YOLOv3 는 SSD 에 비해 총 38/80 개의 계측점에서 더 높은 정확도를 보였다. 나머지 42/80 개의 계측점은 두 알고리즘 간에 정확도에 있어 통계적으로 유의미한 차이를 나타내지 않았다. Error plot 에서는 YOLOv3 가 SSD 에 비해서 error 의 범위가 더 작을 뿐 아니라, 2차원 평면에서 방향성의 영향을 덜 받는 것으로 나타났다. 하나의 영상에서 계측점을 자동 식별하는데 소요된 평균 시간은 YOLOv3 와 SSD 가 각각 0.05 초, 2.89 초로 기록되었다. 본 연구에서 YOLOv3 는 기존 문헌에서 최상의 정확도를 기록했던 연구에 비해 약 5% 가량 높은 정확도를 보였다. 결론: 본 연구를 통해 적용된 두 개의 알고리즘 중, YOLOv3 가 두부계측방사선 사진 계측점 완전 자동 식별의 임상적인 적용에 가능성 높은 알고리즘임을 확인하였다.Abstract Contents I. INTRODUCTION 1 II. MATERIALS AND METHODS 4 1. Subjects 4 2. Manual identification of cephalometric landmarks 4 3. Two Deep Learning Systems 5 4. Test Procedures and Comparisons between the two systems 6 III. RESULTS 7 IV. DISCUSSION 8 V. CONCLUSIONS 13 REFERENCES 14 TABLES 20 FIGURES 24 국문초록 46Docto