3D cephalometric landmark detection by multiple stage deep reinforcement learning

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.ope

Secrets of success:the development of obstetric ultrasound in Scotland, 1963-1990

Abstract available: p.i

Computer-aided cephalometric landmark identification

Master'sMASTER OF ENGINEERIN

An investigation into the use of stereophotogrammetry for the analysis of craniofacial dysmorphology in schizophrenia

Studies of craniofacial dysmorphology in schizophrenia, carried out since the 1960s, have reported minor physical anomalies in those with schizophrenia, prominently in the craniofacial region. Indirect methods, most notably 3D laser imaging, have been used previously for investigating craniofacial dysmorphology in schizophrenia. This project aimed to investigate the ability of a stereophotogrammetry system to detect craniofacial dysmorphology in individuals diagnosed with schizophrenia. Furthermore, observed dysmorphology was characterised and compared with that found in previous studies. Three-dimensional craniofacial landmark coordinates were obtained from images collected using a bespoke design stereophotogrammetry system. The system includes a camera rig and a calibration rig. On the camera rig is mounted three digital single-lens reflex cameras hardwired to a trigger for simultaneous image capture. The calibration rig consists of a frame with strategically positioned retro-reflective calibration markers of known 3D orientation. The precision and reliability of the stereophotogrammetry system was tested using a human subject. Measurements were taken using the system and directly using callipers by two operators on two separate occasions. Intra- and inter-operator precision and inter-modality reliability were calculated and scored. All intra- and inter-operator precision scores were at least below a 7% error, and considered "good". Inter -modality reliability scores had at least a "good" score in 72% of all measurements. Excluding one soft landmark and one landmark with small measurement value, all inter-modality reliability scores were at least "good". The study cohort consisted of 17 African (8 control, 9 schizophrenia) and 13 Caucasian ( 8 control, 5 schizophrenia) males. A set of 18 landmarks focused about the eyes, nose, mouth and chin was identified for each subject and collated in 3D coordinate space. Geometric morphometric analysis - particularly generalised Procrustes analysis and principal component analysis - was carried out on these landmark sets. Discriminant Function Analysis was applied to identify discriminating features in the data set, and classification techniques, aided by feature selection, were applied to separate affected and control subjects. In the African cohort, the results showed wider inward slanting (cat-like) eyes, a wider upturned nose and narrower downturned mouth. In the Caucasian cohort, narrower and wide set eyes, a narrower downturned nose with anteriorly displaced alare, a wider downturned mouth and posteriorly set chin were shown. The Caucasian cohort demonstrates similar dysmorphology as described in the literature. Published data for the African cohort is lacking. The nearest mean and k- nearest neighbour classifiers had the highest accuracy in the African and Caucasian groups respectively, with 71% and 77% correct classification. The efficacy of the stereophotogrammetry system introduced in this study has been shown, with craniofacial dysmorphology in schizophrenia successfully detected. Further studies with larger cohorts are recommended to attempt improved classification accuracy, but a platform now exists to pursue dysmorphology studies in other psychoses, such as bipolar disorder

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

3D soft-tissue, 2D hard-tissue and psychosocial chantes following orthognathic surgery

A 3D imaging system (C3D®), based on the principles of stereophotogrammetry, has been developed for use in the assessment of facial changes following orthognathic surgery. Patients’ perception of their facial appearance before and after orthognathic surgery has been evaluated using standardised questionnaires, but few studies have tried to link this perception with the underlying two-dimensional cephalometric data. Comparisons between patients’ subjective opinions and 3D objective assessment of facial morphology have not been performed. Aims: (1) To test the reliability of the 3D imaging system; (2) to determine the effect of orthognathic surgery on the 3D soft-tissue morphology; (3) to assess skeletal changes following orthognathic surgery; (4) to evaluate soft-tissue to hard-tissue displacement ratios; (5) to ascertain the impact of orthognathic surgery on patients’ perception of their facial appearance and their psychosocial characteristics, (6) to explore the dentofacial deformity, sex and age on the psychosocial characteristics; (7) to evaluate the extent of compatibility between the cephalometric and the three-dimensional measurements and (8) to determine if the magnitude of facial soft-tissue changes affects the perception of facial changes at six months following surgery. Results and Conclusions: C3D imaging system was proved to be accurate with high reproducibility. The reproducibility of landmark identification on 3D models was high for 24 out of the 34 anthropometric landmarks (SD£0.5 mm). One volumetric algorithm in the Facial Analysis Tool had an acceptable accuracy for the assessment of volumetric changes following orthognathic surgery (mean error=0.314 cm3). The error of cephalometric method was low and the simulation of mandibular closure proved to be reproducible. 2D soft-tissue measurements were compatible with 3D measurements in terms of distances, but angular measurements showed significant differences (p<0.05)

Machine Learning for Biomedical Application

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images

Künstliche Intelligenz in der Zahnheilkunde: Scoping-Review und Schließung beobachteter Wissenslücken durch eine methodische und eine klinische Studie

Objectives: The aims of this dissertation were to (1) conduct a scoping review of stud-ies on machine learning (ML) in dentistry and appraise their robustness, (2) perform a benchmarking study to systematically compare various ML algorithms for a specific dental task, and (3) evaluate the influence of a ML-based caries detection software on diagnostic accuracy and decision-making in a randomized controlled trial. Methods: The scoping review included studies using ML in dentistry published between 1st January 2015 and 31st May 2021 on MEDLINE, IEEE Xplore, and arXiv. The risk of bias and reporting quality were assessed with the QUADAS‐2 and TRIPOD checklists, respectively. In the benchmarking study, 216 ML models were built using permutations of six ML model architectures (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network, and Mask Attention Network), 12 model backbones of varying complexities (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169, and Dense-Net201), and three initialization strategies (random, ImageNet, and CheXpert weights). 1,625 dental bitewing radiographs were used for training and testing. Five-fold cross-validation was carried out and model performance assessed using F1-score. In the clin-ical trial, each one of 22 dentists examined 20 randomly selected bitewing images for proximal caries; 10 images were evaluated with ML and 10 images without ML. Accura-cy in lesion detection and the suggested treatment were evaluated. Results: The scoping review included 168 studies, describing different ML tasks, mod-els, input data, methods to generate reference tests, and performance metrics, imped-ing comparison across studies. The studies showed considerable risk of bias and mod-erate adherence to reporting standards. In the benchmarking study, more complex models only minimally outperformed their simpler counterparts, if at all. Models initial-ized by ImageNet or CheXpert weights outperformed those using random weights (p<0.05). The clinical trial demonstrated that dentists using ML showed increased accu-racy (area under the receiver operating characteristic [mean (95% confidence interval): 0.89 (0.87–0.90)]) compared with those not using ML [0.85 (0.83–0.86); p<0.05], pri-marily due to their higher sensitivity [0.81 (0.74–0.87) compared to 0.72 (0.64–0.79); p<0.05]. Notably, dentists using ML also showed a higher frequency of invasive treat-ment decisions than those not using it (p<0.05). Conclusion: To facilitate comparisons across ML studies in dentistry, a minimum (core) set of outcomes and metrics should be developed, and researchers should strive to improve robustness and reporting quality of their studies. ML model choice should be performed on an informed basis, and simpler models may often be similarly capable as more complex ones. ML can increase dentists’ diagnostic accuracy but also lead to more invasive treatment.Ziele: Die Ziele dieser Dissertation waren, (1) ein Scoping-Review von Studien über maschinelles Lernen (ML) in der Zahnmedizin, (2) eine Benchmarking-Studie zum systematischen Vergleich verschiedener ML-Algorithmen für eine bestimmte zahnmedizinische Aufgabe, und (3) eine randomisierte kontrollierte Studie zur Bewertung einer ML-basierten Karies-Erkennungssoftware bezüglich diagnostischer Genauigkeit und Einfluss auf den Entscheidungsprozess durchzuführen. Methoden: Das Scoping-Review umfasste Studien über ML in der Zahnmedizin, veröffentlicht vom 1. Januar 2015 bis 31. Mai 2021 auf MEDLINE, IEEE Xplore und arXiv. Bias-Risiko und Berichtsqualität wurden mit den Checklisten QUADAS-2 beziehungsweise TRIPOD bewertet. In der Benchmarking-Studie wurden 216 ML-Modelle durch Permutationen von sechs Architekturen (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network und Mask Attention Network), 12 Backbones (Res-Net18, ResNet34, ResNet50, ResNet101, ResNet152, VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169 und DenseNet201) und drei Initialisierungsstrategien (zufällige-, ImageNet- und CheXpert-Gewichtungen) erstellt. Zum Training und Testen wurden 1.625 Bissflügel-Röntgenaufnahmen genutzt. Es wurde eine fünffache Kreuzvalidierung durchgeführt und die Modellleistung anhand des F1-Scores bewertet. In der klinischen Studie untersuchten 22 Zahnärzte jeweils 20 zufällig ausgewählte Bissflügelbilder auf Approximalkaries; 10 Bilder wurden mit und 10 Bilder ohne ML ausgewertet. Die Genauigkeit in der Erkennung von Läsionen sowie die abgeleitete Therapieempfehlung wurden bewertet. Ergebnisse: Das Scoping-Review schloss 168 Studien ein, in denen verschiedene ML-Aufgaben, Modelle, Eingabedaten, Methoden zur Generierung von Referenztests und Leistungsmetriken beschrieben wurden. Die Studien zeigten ein erhebliches Bias-Risiko und eine mäßige Einhaltung der Berichtsstandards. In der Benchmarking-Studie hatten komplexere Modelle gegenüber einfachen Modellen allenfalls geringe Vorteile. Mit ImageNet- oder CheXpert-Gewichtungen initialisierte Modelle übertrafen solche mit Zufallsgewichtungen (p<0,05). In der klinischen Studie erreichten Zahnärzte mit ML eine höhere Genauigkeit bei der Kariesdetektion (Receiver-Operating-Charakteristik [Mittelwert (95 % Konfidenzintervall) 0,89 (0,87–0,90)]) als ohne ML [0,85 (0,83–0,86); p<0,05], hauptsächlich aufgrund höherer Sensitivität [0,81 (0,74–0,87) verglichen mit 0,72 (0,64–0,79); p<0,05]. Zahnärzte mit ML wählten auffallend häufiger invasive Behandlungen als ohne ML (p<0,05). Schlussfolgerung: Zur besseren Vergleichbarkeit von ML-Studien in der Zahnmedizin, sollten Core Outcomes und Metriken definiert sowie Robustheit und Berichtsqualität verbessert werden. Die Entwicklung von ML-Modellen sollte auf informierter Basis erfolgen, bei oft ähnlicher Leistung von einfacheren und komplexeren Modellen. ML kann die diagnostische Genauigkeit erhöhen, aber auch zu mehr invasiven Behandlungen führen