Objectives:To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR).Methods:Studies using application...

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The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.

To honor the enormous contribution that the applied mathematician Lee Segel made to the Springer journal Bulletin of Mathematical Biology (BMB) and the field of mathematical biology as a whole, Springer, in partnership with the Society of Mathematical Biology (SMB), is funding a series of prizes based on papers published in the BMB. This year’s prize for the best original research paper has been awarded to Tomas de-Camino-Beck and Mark Lewis for their paper A new method for calculating net reproductive rate from graph reduction with applications to the control of invasive species1. The prize for the best student research paper went to Emma Jin and Christian Reidys for their paper Asymptotic enumeration of RNA structures with pseudoknots2. The awards were presented at the annual conference of the SMB, which took place in Toronto, Canada, from 30 July to 2 August 2008. The winner of the prize for the best review paper will be announced at the 2009 SMB conference.

Книги издательства Springer Science & Business Media

Objective: To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks. Materials and Methods...

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Automated identification of cephalometric landmarks: Part 1—Comparisons between the latest deep-learning methods YOLOV3 and SSD

Objectives: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Onl...

/pdf/automated-identification-of-cephalometric-landmarks-part-2-12al3b4sd3.pdf

Automated identification of cephalometric landmarks: Part 2-Might it be better than human?

Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. Given a target image, a template is first oriented, repositioned, and scaled to the target during a scaled rigid registration step, then transformed further to fit the specific shape of the target using a non-rigid transformation. As validation, we use n = 41 3D facial images to demonstrate that the MeshMonk registration is accurate, with 1.26 mm average error, across 19 landmarks, between placements from manual observers and using the MeshMonk toolbox. We also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks, the MeshMonk toolbox produces a dense mesh of vertices across the entire surface, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history.

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Jesús Montúfar

Papers

Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections.

Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes