PWD-3DNet: A deep learning-based fully-automated segmentation of multiple structures on temporal bone CT scans

The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among crit- ical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline as the first fully automated algorithm for segmenting multiple temporal bone structures from CT volumes, including the sigmoid sinus, facial nerve, inner ear, malleus, incus, stapes, internal carotid artery and internal auditory canal. The proposed fully convolutional network, PWD-3DNet, is a patch-wise densely connected (PWD) three-dimensional (3D) network. The accuracy and speed of the proposed algorithm was shown to surpass current manual and semi-automated segmentation techniques. The experimental results yielded significantly high Dice similar- ity scores and low Hausdorff distances for all temporal bone structures with an average of 86% and 0.755 millimeter (mm), respectively. We illustrated that overlapping in the inference sub-volumes improves the segmentation performance. Moreover, we proposed augmentation layers by using samples with various transformations and image artefacts to increase the robustness of PWD-3DNet against image acquisition protocols, such as smoothing caused by soft tissue scanner settings and larger voxel sizes used for radiation reduction. The proposed algorithm was tested on low-resolution CTs acquired by another center with different scanner parameters than the ones used to create the algorithm and shows potential for application beyond the particular training data used in the study

Applications of artificial intelligence in dentistry: A comprehensive review

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods: The comprehensive review was conducted in MEDLINE/ PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions: The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance: The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00 PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU

PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans

The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among critical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline as the first fully automated algorithm for segmenting multiple temporal bone structures from CT volumes, including the sigmoid sinus, facial nerve, inner ear, malleus, incus, stapes, internal carotid artery and internal auditory canal. The proposed fully convolutional network, PWD-3DNet, is a patch-wise densely connected (PWD) three-dimensional (3D) network. The accuracy and speed of the proposed algorithm was shown to surpass current manual and semi-automated segmentation techniques. The experimental results yielded significantly high Dice similarity scores and low Hausdorff distances for all temporal bone structures with an average of 86% and 0.755 millimeter (mm), respectively. We illustrated that overlapping in the inference sub-volumes improves the segmentation performance. Moreover, we proposed augmentation layers by using samples with various transformations and image artefacts to increase the robustness of PWD-3DNet against image acquisition protocols, such as smoothing caused by soft tissue scanner settings and larger voxel sizes used for radiation reduction. The proposed algorithm was tested on low-resolution CTs acquired by another center with different scanner parameters than the ones used to create the algorithm and shows potential for application beyond the particular training data used in the study

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

From bench to bedside - current clinical and translational challenges in fibula free flap reconstruction.

Fibula free flaps (FFF) represent a working horse for different reconstructive scenarios in facial surgery. While FFF were initially established for mandible reconstruction, advancements in planning for microsurgical techniques have paved the way toward a broader spectrum of indications, including maxillary defects. Essential factors to improve patient outcomes following FFF include minimal donor site morbidity, adequate bone length, and dual blood supply. Yet, persisting clinical and translational challenges hamper the effectiveness of FFF. In the preoperative phase, virtual surgical planning and artificial intelligence tools carry untapped potential, while the intraoperative role of individualized surgical templates and bioprinted prostheses remains to be summarized. Further, the integration of novel flap monitoring technologies into postoperative patient management has been subject to translational and clinical research efforts. Overall, there is a paucity of studies condensing the body of knowledge on emerging technologies and techniques in FFF surgery. Herein, we aim to review current challenges and solution possibilities in FFF. This line of research may serve as a pocket guide on cutting-edge developments and facilitate future targeted research in FFF

Assessment of bone loss adjacent to lower second molar in case of third molar impaction and other findings using Orthopantomography (OPG)

[eng] INTRODUCTION: Dental impaction is a pathological situation in which a tooth is totally or partially included in the jaw or maxilla bone. Different teeth are noticeably prone to impaction phenomena, including the canine and upper third molar, third molar, and lower canine. However, the lower third molar is considered the most common impacted tooth, accounting for 98% compared to other impacted teeth. It usually erupts between the ages of 17 and 24. The overall prevalence of impaction of the lower third molar is estimated to be around 24%, with no gender predilection between men and women. There are factors that cause a tooth to not erupt in the expected time, including lack of space, poorly positioned dental germs, abnormal eruption pathway, and alterations in jaw development. Mandibular third molar impaction has many complications in the adjacent soft tissue and second molar. Thus, pericoronitis is one of the consequences caused by impaction, which manifests itself as inflammation of the gum tissue that covers it. Other complications include distal caries, bone loss adjacent to the second molar, cystic formation, and neoplastic changes. The different positions of impacted mandibular third molars can complicate the maintenance of oral hygiene and control plate. Therefore, the periodontium manifests the formation of pockets that facilitate bone loss on the distal aspect of the second molar. In addition, the mandibular third molar is located near the inferior mandibular canal which contains the inferior alveolar nerve, the artery and the corresponding vein. Surgical extraction of such a tooth may pose a risk of nerve injury leading to dysesthesia or paresthesia. In addition, the third molar removal procedure can put pressure on the bone that can lead to fracture of the angle of the jaw. Finally, extraction can affect the lower 2nd molar. Therefore, clinical and diagnostic procedures are essential to address the position of the impacted tooth, the associated pathology, the proximity to the nerve canal, and the decision to intervene. Orthopantomography (OPG) is an imaging technique routinely used in the dental office, representing the jawbone and jaw in a single image. It has the advantage of exposing dental and bone changes in the oral cavity, including impacted teeth. Among the advantages of OPG are its speed and ease, especially the 2nd molar and the canal inside the realization, better patient cooperation and acceptance, complete coverage of dental arches and related structures (more anatomical structures can be seen on a panoramic film than in a full series of intraoral x-rays), simplicity, and low radiation exposure for the patient, compared to the most advanced imaging tool, cone beam computed tomography (CBCT). As impaction of the third molar is one of the dental pathologies that dentists frequently see, the determination of the position and relationship with nearby structures, especially the 2nd molar and the lower dental canal, can be predicted using OPG. HYPOTHESIS: Impaction of the third molar causes bone loss distal to the second molar, therefore there will be bone gain distal to the second molar after extraction of the third molar. OBJECTIVE: To determine the distal bone loss of the lower second molar associated with impaction of the third molar and to analyze its evolution after the extraction of the third molar.[spa] INTRODUCCIÓN: la impactación dental es una situación patológica en la que un diente se incluye total o parcialmente en el hueso de la mandíbula o del maxilar. Diferentes dientes son notablemente propensos a los fenómenos de impactación, incluyendo el canino y el tercer molar superior, el tercer molar y el canino inferior. Sin embargo, el tercer molar inferior se considera el diente impactado más común, representando el 98% en comparación con otros dientes impactados. Por lo general, entra en erupción entre los 17 y 24 años. La prevalencia global de impactación del tercer molar inferior se estima en torno al 24%, sin predilección de género entre hombres y mujeres. Hay factores que hacen que un diente no erupcione en el tiempo esperado, incluida la falta de espacio, los gérmenes dentales mal posicionados, la vía de erupción anormal y las alteraciones del desarrollo de la mandíbula. La impactación del tercer molar mandibular tiene muchas complicaciones en el tejido blando adyacente y en el segundo molar. Así, la pericoronaritis es una de las consecuencias causadas por la impactación, que se manifiesta como inflamación del tejido gingival que la recubre. Otras complicaciones incluyen la caries distal, la pérdida ósea adyacente al segundo molar, formación quística y cambios neoplásicos. Las diferentes posiciones de los terceros molares mandibulares impactados pueden complicar el mantenimiento de la higiene oral y la placa control. Por lo tanto, el periodonto manifiesta la formación de bolsas que facilitan la perdida oseas en la cara distal del segundo molar. Además, el tercer molar mandibular está ubicado cerca del canal mandibular inferior el cual contiene el nervio alveolar inferior, la arteria y la vena correspondiente. La extracción quirúrgica de dicho diente puede ejercer un riesgo de lesión nerviosa que conduzca a disestesia o a parestesia. Además, el procedimiento de extracción del tercer molar puede ejercer una presión sobre el hueso que puede conducir a la fractura del ángulo de la mandíbula. Finalmente, la extracción puede afectar al 2º molar inferior. Por lo tanto, los procedimientos clínicos y diagnósticos son esenciales para abordar la posición del diente impactado, la patología asociada, la proximidad al canal nervioso y la decisión de intervención. La ortopantomografía (OPG) es una técnica de diagnóstico por la imagen utilizada rutinariamente en el consultorio dental, que representa al maxilar y la mandíbula en una sola imagen. Tiene la ventaja de exponer los cambios dentales y óseos en la cavidad oral, incluidos los dientes impactados. Entre las ventajas de la OPG, se encuentran su rapidez y facilida, en especial el 2º molar y el canal dentro de realización, mejor cooperación y aceptación del paciente, cobertura completa de las arcadas dentales y estructuras relacionadas (se pueden ver más estructuras anatómicas en una película panorámica que en una serie completa de radiografías intraorales), simplicidad y baja exposición a la radiación para el paciente, en comparación con la herramienta de imagen más avanzada, la tomografía computarizada de haz cónico (CBCT). Como la impactación del tercer molar es una de las patologías dentales que los odontólogos ven con frecuencia, la determinación de la posición y la relación con las estructuras cercanas, en especial el 2º molar y el canal dentario inferior, se puede predecir utilizando OPG. HIPÓTESIS: La impactación del tercer molar causa pérdida ósea distal al segundo molar, por lo tanto, habrá ganancia ósea distal al segundo molar después de la extracción del tercer molar. molar impactado. OBJETIVO: determinar la pérdida ósea distal del segundo molar inferior asociada a impactación del tercer molar y analizar su evolución tras la extracción del tercer molar