Morphometric and Volumetric Measurements of Orbit With Cone-Beam Computed Tomography

Sarkar, Souvick; Baliga, Mohan; Prince, Jefferson; Ongole, Ravikiran; Natarajan, Srikant

doi:10.1016/j.joms.2020.10.026

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Notably, areas around the nose, mouth and periorbital zones were also highlighted as crucial for age prediction by CNNs with VGG‐16 architecture pretrained on IMDB‐WIKI or ImageNet, corroborating our results 57 . In morphometric studies, the sizes of the infrapalpebral sulcus, the zone of eyelids and supraorbital notch, the rhinion, philtrum, zone of lips commissure and glabella have been reported to correlate with chronological age 69–71 . The importance of infrapalpebral sulcus, cheeks/cheekbones and forehead has been shown for age prediction from facial images by AI algorithms 72 .…”

Section: Discussionsupporting

confidence: 85%

“…20 57 In morphometric studies, the sizes of the infrapalpebral sulcus, the zone of eyelids and supraorbital notch, the rhinion, philtrum, zone of lips commissure and glabella have been reported to correlate with chronological age. [69][70][71] The importance of infrapalpebral sulcus, cheeks/cheekbones and forehead has been shown for age prediction from facial images by AI algorithms. 72 In a study on Chinese males, a significant correlation was observed between actual age and age predicted by CNNs from nasolabial sulcus and wrinkles in the zone of forehead, infrapalpebral sulcus and glabella.…”

Section: Possible Effects Of Lifestyle On Age Prediction From Hand An...mentioning

confidence: 99%

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Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

Georgievskaya,

Tlyachev,

Kiselev

et al. 2024

Experimental Dermatology

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Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold telltale signs of a person's age. To determine the utility of using only hand images in predicting CA, we developed two deep CNNs based on 1) dorsal hand images (H) and 2) frontal face images (F). Subjects (n = 1454) were Indian women, 20–80 years, across three geographic cohorts (Mumbai, New Delhi and Bangalore) and having a broad variation in skin tones. Images were randomised: 70% of F and 70% of H were used to train CNNs. The remaining 30% of F and H were retained for validation. CNN validation showed mean absolute error for predicting CA using F and H of 4.1 and 4.7 years, respectively. In both cases correlations of predicted and actual age were statistically significant (r(F) = 0.93, r(H) = 0.90). The CNNs for F and H were validated for dark and light skin tones. Finally, by blurring or accentuating visible features on specific regions of the hand and face, we identified those features that contributed to the CNN models. For the face, areas of the inner eye corner and around the mouth were most important for age prediction. For the hands, knuckle texture was a key driver for age prediction. Collectively, for AI estimates of CA, CNNs based solely on hand images are a viable alternative and comparable to CNNs based on facial images.

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Section: Discussionsupporting

confidence: 85%

Section: Possible Effects Of Lifestyle On Age Prediction From Hand An...mentioning

confidence: 99%

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

Georgievskaya,

Tlyachev,

Kiselev

et al. 2024

Experimental Dermatology

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“…The selection of LMs used in the present study was guided by earlier work (Khonsari et al, 2016; Rontal et al, 1979; Sarkar et al, 2021). The repeatability (i.e.…”

Section: Methodsmentioning

confidence: 99%

Quantification of global orbital shape variation

Prevost

Müller²,

Lauwers

et al. 2023

Clinical Anatomy

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The complex anatomy of the orbit generates a complex orbital shape that can only be quantified approximatively by classic linear measurements such as maximum width and height. There is no global three‐dimensional quantification of variations in orbital shape. The purpose of this study was to develop a method to quantify a global three‐dimensional orbital shape variation in a healthy population and to test a series of explanatory factors. We investigated the hypotheses that orbital shape is related to gender(H1), orbital size(H2) and/or age(H3). Medical computed tomography(CT) images of 60 adult individuals were studied. The study sample consisted of 30 males and 30 females with a mean age of 25.1 years. Four anatomical landmarks and 140 semi‐landmarks were measured on both positive and negative 3D reconstructed orbits and analyzed with geometric morphometrics. A principal component analysis(PCA) was computed to define a morphological space. Shape variation was visualized using vector distance maps and diagrams. The greatest variation was seen in the length of the superior orbital fissure. There was a gradient in terms of orbital shape ranging from short, wide orbits to tall, narrow orbits. The analysis did not highlight any significant age‐, gender‐ or size‐related impact in terms of orbital shape variation. Future avenues to explore include the study of other potential explanatory factors such as the different embryological origins of the orbital bones, the passage of vessels and nerves, and ethnic origins. This method can also be applied to the study of pathological orbits.

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Anatomy of the Orbit: Overall Skeletal and Topographical Configuration

Cornelius,

Gooris

2023

Surgery in and Around the Orbit

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Objective: The intent of this chapter is to provide a systematic reappraisal of the bony anatomy of the orbit. The studies and the available literature on this topic are ever-expanding. The present knowledge shall be put into perspective.Material and Methods: Textbook and atlas descriptions served as a starting point for a comprehensive outline of the osseous anatomy of the orbits. To deepen the insight and understanding, a small selection of recent reports on specific structures has been reviewed. For visual purposes, photographs of PMHS (post mortem human subject) specimens and illustrations were used.Review/Results: The most distinctive feature of each orbit is the quadrangular pyramidal shape with a tetrahedron spire making up the orbital apex. The bony elements of the orbits are delineated with the sphenoid as backward foundation and starting base for the overall architectural composition. A closer look goes into the openings within the orbital precincts and reveals the subtleties and variations of the fissures, canals, grooves, foramina, notches, and fossae monitoring recent publications. The research modalities of these studies have shifted from dry skull investigations and PMHS dissections to modern imaging techniques (CT, MRI, CBCT) within defined living populations facilitating the analysis of hidden spaces or hard-to-reach structures in a non-destructive manner. Additional advantages of imaging include quantification and a detailed morphometric evaluation in the spirit of computational anatomy.Conclusion: In essence, the combination of traditional anatomic knowledge and the understanding of the tremendous complexity of variations characterized in new publications demands for a patient-specific diagnostic workup as exemplified in this book. As a matter of fact, however it is more likely to recognize and grasp features that one knows or knows about from previous intensive learning.

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Morphometric and Volumetric Measurements of Orbit With Cone-Beam Computed Tomography

Cited by 5 publications

References 19 publications

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

Quantification of global orbital shape variation

Anatomy of the Orbit: Overall Skeletal and Topographical Configuration

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