Marina Codari

Computer-aided cephalometric landmark annotation for CBCT data

PurposeNowadays, with the increased diffusion of Cone Beam Computerized Tomography (CBCT) scanners in dental and maxillo-facial practice, 3D cephalometric analysis is emerging. Maxillofacial surgeons and dentists make wide use of cephalometric analysis in diagnosis, surgery and treatment planning. Accuracy and repeatability of the manual approach, the most common approach in clinical practice, are limited by intra- and inter-subject variability in landmark identification. So, we propose a computer-aided landmark annotation approach that estimates the three-dimensional (3D) positions of 21 selected landmarks.MethodsThe procedure involves an adaptive cluster-based segmentation of bone tissues followed by an intensity-based registration of an annotated reference volume onto a patient Cone Beam CT (CBCT) head volume. The outcomes of the annotation process are presented to the clinician as a 3D surface of the patient skull with the estimate landmark displayed on it. Moreover, each landmark is centered into a spherical confidence region that can help the clinician in a subsequent manual refinement of the annotation. The algorithm was validated onto 18 CBCT images.ResultsAutomatic segmentation shows a high accuracy level with no significant difference between automatically and manually determined threshold values. The overall median value of the localization error was equal to 1.99 mm with an interquartile range (IQR) of 1.22–2.89 mm.ConclusionThe obtained results are promising, segmentation was proved to be very robust and the achieved accuracy level in landmark annotation was acceptable for most of landmarks and comparable with other available methods.

Implant-Supported Immediately Loaded Full-Arch Rehabilitations: Comparison of Resin and Zirconia Clinical Outcomes in a 5-Year Retrospective Follow-Up Study.

Purpose:We analyzed complications and failures of final full-arch implant-supported rehabilitations, comparing resin and zirconia prosthesis materials. Prostheses were retrospectively followed up for 5 years. Materials and Methods:One hundred twenty-five patients who received one or two four to six implant-supported immediately loaded full-arch rehabilitations in resin (166 prostheses) or zirconia (48 prostheses) were analyzed. Results:One hundred thirteen patients (53 men, 60 women), with 214 full-arch prostheses (105 maxillary, 109 mandibular), were analyzed. During the follow-up interval, the prosthesis annual complication rate was 6.6%, free complications survival was 75.5% (60 months). Age, number of implants, and prosthesis material did not influence complication risk. Men had a higher risk of complications than women. Prosthesis annual failure rate was 4.6%, free survival was 85.5% (60 months). Age, number of implants, and prosthesis material did not influence failure risk. Men and maxillary arch prostheses had a higher risk of failures than women and mandibular arch prostheses. Conclusion:Implant-supported, immediately loaded full-arch rehabilitations supporting resin or zirconia based prostheses were clinically successful in a 5-year follow-up. Prosthesis material did not influence complication risk.

Facial Evaluation in Holoprosencephaly.

Abstract Holoprosencephaly (HPE) is a malformation of the brain, occurring during the first weeks of pregnancy, that may be associated with several craniofacial alterations and different pathological conditions. The authors describe a 2-year-old girl with lobar HPE, epilepsy, but with a roughly normal face. Despite the macroscopic, clinical appearance, a facial morphometric analysis, performed through a stereophotogrammetric system, showed features that diverge from reference subjects and that are considered typical of HPE. This study highlights how a digital anthropometric facial assessment through stereophotogrammetry can be a useful and noninvasive instrument to investigate the facial features of HPE, especially in the presence of an apparently normal facial aspect. Additionally, it can provide the bases for future insights about the relationship between embryological facial and cerebral development, the time of the occurred defect and, in the end, enrich basic scientific knowledge.

The face of Glut1-DS patients: A 3D craniofacial morphometric analysis

Glut1 deficiency syndrome (Glut1‐DS) is a neurological and metabolic disorder caused by impaired transport of glucose across the blood brain barrier (BBB). Mutations on the SCL2A1 gene encoding the glucose transporter protein in the BBB cause the syndrome, which encompasses epilepsy, movement disorders, and mental delay. Such variability of symptoms presents an obstacle to early diagnosis. The patients seem to share some craniofacial features, and identification and quantification of these could help in prompt diagnosis and clinical management. We performed a three‐dimensional morphometric analysis of the faces of 11 female Glut1‐DS patients using a stereophotogrammetric system. Data were analyzed using both inter‐landmark distances and Principal Component Analysis. Compared with data collected from age‐, sex‐, and ethnicity‐matched control subjects, common and homogenous facial features were identified among patients, which were mainly located in the mandible and the eyes. Glut1‐DS patients had a more anterior chin; their mandibular body was longer but the rami were shorter, with a reduced gonial angle; they had smaller and down‐slanted eyes with a reduced intercanthal distance. This study highlights the importance of morphometric analysis for defining the facial anatomical characteristics of the syndrome better, potentially helping clinicians to diagnose Glut1‐DS. Improved knowledge of the facial anatomy of these patients can provide insights into their facial and cerebral embryological development, perhaps further clarifying the molecular basis of the syndrome. Clin. Anat. 30:644–652, 2017.

Multi-segmental movements as a function of experience in karate

ABSTRACT Karate is a martial art that partly depends on subjective scoring of complex movements. Principal component analysis (PCA)-based methods can identify the fundamental synergies (principal movements) of motor system, providing a quantitative global analysis of technique. In this study, we aimed at describing the fundamental multi-joint synergies of a karate performance, under the hypothesis that the latter are skilldependent; estimate karateka’s experience level, expressed as years of practice. A motion capture system recorded traditional karate techniques of 10 professional and amateur karateka. At any time point, the 3D-coordinates of body markers produced posture vectors that were normalised, concatenated from all karateka and submitted to a first PCA. Five principal movements described both gross movement synergies and individual differences. A second PCA followed by linear regression estimated the years of practice using principal movements (eigenpostures and weighting curves) and centre of mass kinematics (error: 3.71 years; R2 = 0.91, P ≪ 0.001). Principal movements and eigenpostures varied among different karateka and as functions of experience. This approach provides a framework to develop visual tools for the analysis of motor synergies in karate, allowing to detect the multi-joint motor patterns that should be restored after an injury, or to be specifically trained to increase performance.

3D Craniofacial Morphometric Analysis of Young Subjects with Marfan Syndrome: A Preliminary Report

Marfan syndrome (MFS) is a rare autosomic dominant disease of connective tissues mostly due to mutations in the fibrillin 1 gene. Clinical manifestations of MFS include a variety of signs and symptoms, mainly affecting the heart, blood vessels, bones, joints and eyes, and comprising craniofacial alterations. At present, diagnosis of MFS is largely based on clinical signs and family history. However, it could may be difficult, as its manifestations vary greatly and they are not always present right away. Since a life-threatening complication of MFS is aortic dissection, an early diagnosis of the disorder is essential. We aim to better describe the face of patients with MFS, identifying new quantitative morphological features which could facilitate the early diagnosis of the disease. In the current preliminary study, a group of young subjects with MFS was investigated. Three-dimensional facial images of 3 girls and 8 boys aged 5-15 years were collected by stereophotogrammetry. From the coordinates of 50 anatomical facial landmarks, linear distances and angles were measured; z score values were calculated through the comparison with data obtained from 556 control subjects matched for gender, age, and ethnicity. All subjects with MFS showed a longer face than controls, mainly due to an increased middle third (mean z score = 1.7). They also showed a longer mandibular body (mean z score = 1.4) with a shorter ramus (mean z score = -1.4) and a greater facial divergence (mean z score = 2.2). The assessment of facial features of subjects with MFS pointed out some morphometric characteristics that had never been reported in literature, alongside with other well known alterations, and suggests the usefulness of a three-dimensional quantitative approach for the recognition of facial phenotypic features of the syndrome. Nevertheless, they need to be confirmed extending the study on more patients.

Stereophotogrammetric Evaluation of Labial Symmetry After Surgical Treatment of a Lymphatic Malformation

Abstract Lymphatic malformations (LMs) are rare, nonmalignant masses, frequently involving the head and neck, potentially causing impairment to the surrounding anatomical structures. Major LMs frequently cause facial disfigurement with obvious consequences on self-esteem and social functioning. The attempt to restore symmetry is thus one of the main goals of treatment. In this study, the authors present a not-invasive method to objectively quantify the symmetry of the labial area before and after surgical treatment of a LM, affecting a 16-year-old woman. This was done with sequential three-dimensional stereophotogrammetric imaging and morphometric measurements. The method showed a high reproducibility and supplied quantitative indicators of the local degree of symmetry, helping clinicians in its objective assessment, and facilitating treatment planning and evaluation. A quantitative appraisal of the results can additionally improve patient adherence to a usually multistage therapy.

The nasal septum deviation index (NSDI) based on CBCT data

OBJECTIVE To assess whether three-dimensional morphometric parameters could be useful in nasal septal deviation (NSD) diagnosis and, secondarily, whether CBCT could be considered an adequate imaging technique for the proposed task. METHODS We analysed images of 46 subjects who underwent CBCT for reasons not related to this study. Two experienced operators divided all the images into healthy and NSD subjects. Subsequently, the images were segmented using ITK Snap in order to obtain the three-dimensional model of the nasal airways and compute four morphological parameters: septal deviation angle (SDA), percentage of volume difference between right and left side of the nasal airways, nasal airway total volume and a new synthetic septal deviation index (SDI). Principal component analysis (PCA) was used to unveil relationships between each variable and the global nasal airway variability. RESULTS Differences between the groups were found in SDA (p < 0.001), in volume percentage difference (p < 0.05) and in SDI (p < 0.001). PCA showed high correlation between the SDI and the first principal component (0.97, p < 0.001). CONCLUSIONS Among the analysed parameters, SDI seemed to be the most suitable for the quantitative assessment of NSD, and CBCT allowed accurate assessment of airway morphology.

Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States

Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place. The European Union (EU) is reforming these fields with new legislation (General Data Protection Regulation [GDPR], Cybersecurity Directive, Medical Devices Regulation, In Vitro Diagnostic Medical Device Regulation). This reform is gradual, but it has now made its first impact, with the GDPR and the Cybersecurity Directive having taken effect in May, 2018. As regards the United States (U.S.), the regulatory scene is predominantly controlled by the Food and Drug Administration. This paper considers issues of accountability, both legal and ethical. The processes of medical device decision-making are largely unpredictable, therefore holding the creators accountable for it clearly raises concerns. There is a lot that can be done in order to regulate AI applications. If this is done properly and timely, the potentiality of AI based technology, in radiology as well as in other fields, will be invaluable.Teaching Points• AI applications are medical devices supporting detection/diagnosis, work-flow, cost-effectiveness.• Regulations for safety, privacy protection, and ethical use of sensitive information are needed.• EU and U.S. have different approaches for approving and regulating new medical devices.• EU laws consider cyberattacks, incidents (notification and minimisation), and service continuity.• U.S. laws ask for opt-in data processing and use as well as for clear consumer consent.

Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6–9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.