Machine Learning-Based Facial Beauty Prediction and Analysis of Frontal Facial Images Using Facial Landmarks and Traditional Image Descriptors

Facial Aesthetic Ideals: A Literature Summary of Supporting Evidence

BACKGROUND

To achieve the goal of enhancing facial beauty it is crucial for aesthetic physicians and plastic surgeons to have a deep understanding of aesthetic ideals. Although numerous aesthetic criteria have been proposed over the years, there is a lack of empirical analysis supporting many of these standards.

OBJECTIVES

This aim of this review was to undertake the first exploration of the empirical evidence concerning the aesthetic ideals of the face in the existing literature.

METHODS

A comprehensive search in MEDLINE, Embase, Scopus and CENTRAL databases was conducted for primary clinical studies reporting on the classification of the facial aesthetic units as per the Gonzales-Ulloa facial aesthetic unit classification from January 1962 to November 2022.

RESULTS

A total of 36 articles were included in the final review: 12 case series, 14 cohort studies, and 10 comparative studies. These described the aesthetic ideals of the following areas: forehead (6 studies; mean level of evidence, 3.33); nose (9 studies; mean level of evidence, 3.6); orbit (6 studies; mean level of evidence, 3); cheek (4 studies; mean level of evidence, 4.07); lips (6 studies; mean level of evidence, 3.33); chin (4 studies; mean level of evidence, 3.75); ear (1 study; level of evidence, 4).

CONCLUSIONS

The units that were most extensively studied were the nose, forehead, and lip, and these studies also appeared in journals with higher impact factors than other subunits. Conversely, the chin and ear subunits had the fewest studies conducted on them and had lower impact factors. To provide a useful resource for readers, it would be prudent to identify and discuss influential papers for each subunit.

LEVEL OF EVIDENCE: 3

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

Puzzle Facelift: A non-surgical, whole treatment for facial modelling and rejuvenation

The assessment of correct anatomy of face and the evaluation of the dynamic interplay between anatomy and function, in order to get an overall improvement, is a leading topic in aesthetic surgery and has increased its impact in the recent years. A non-surgical impact technique reduces both post-treatment discomfort and the overall cost. Obtaining the simultaneous correction in forehead, nose, lips and chin means obtaining an overall improvement in face view. Likewise, the high predictability of the procedure and the absence of alloplastic implant, bone re-absorption and scars have to be considered in non-surgical aesthetic medicine. In conclusion, a single-session of profile correction using HA filler should be proposed to patient, every time the aesthetic practitioner sees a coexistence of forehead, nose lips and chin defects.

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.