Facial Analysis Research Articles

Driver body condition monitoring system based on human-computer interaction

Tired driving is still a significant cause of traffic accidents, often causing huge economic losses to society.Therefore, how to avoid driver fatigue has become a meaningful problem. The purpose of this paper is to review and summarize various research methods of driver fatigue monitoring system at home and abroad, so as to reduce the probability of traffic accidents caused by fatigue driving. The main content of this paper is to review the existing fatigue driving monitoring technologies such as electrocardiogram (ECG), electroencephalogram (EEG), HRV, eye tracking system, CNN, facial state analysis, emotion recognition, 5G and brain wave recognition, and infer the fatigue level of drivers by using these technologies to extract various clues that usually represent the level of human vigilance. Assess cognitive load and identify emotional states to monitor tired driving. By discussing the advantages and disadvantages of various fatigue monitoring systems at home and abroad, this paper proposes an innovative method, which transforms the previous single-mode monitoring system into a multi-mode monitoring system, integrates multiple biosensors, extracts relevant features from the biosensor data and processes the data reasonably in order to understand the drivers physiological state more comprehensively. This method is expected to improve the accuracy of fatigue monitoring system.

A methodology for skull reconstruction

The reconstruction process of the skull and skeleton represents a critical step in the study of fossil vertebrates and is often responsible for how the evolutionary history of a taxon is interpreted. It is also an important step in any test of the researcher’s understanding of the anatomy of the fossil. Even so, there is no recognized method for its completion, leaving the actual process to the individual undertaking the investigation. While this is often accomplished in an appropriate manner, there remains extensive room for error. With the onset of cladistic methodology dating back to the 1980s, character state description has become ever more critical in establishing phylogenetic histories, and over a period of 40 years, character state interpretation has often relied on specimen drawings and reconstructions. Based on a career dedicated to the detailed skeletal reconstruction of tetrapods, the content presented here highlights a stepwise approach that is designed to minimize error and increase the value of fossil reconstructions. We describe the skull reconstruction of the Palaeozoic tetrapod Limnoscelis paludis Williston, 1911 and highlight some of the more critical strategies that are necessary to maximize accuracy and hence increase phylogenetic reliability as well as support opportunities for testing anatomical interpretations as well as functional and ultimately behavioural interpretations. We also take the opportunity to highlight the extensive career contributions made to the field of palaeontology by Diane Scott, who for over 40 years has represented the nec plus ultra of fossil preparation, illustration, and reconstruction.

Temporomandibular joint disorders in skeletal class II patients referred to orthognathic surgery: A cross-sectional study.

The aim of this study was to assess the prevalence of Temporomandibular Disorders (TMD) in subjects with skeletal class II dentofacial deformity referred for orthognathic surgery, as well as to elucidate its association with sociodemographic and psychosocial features. This was a cross-sectional study using the Research Diagnostic Criteria for Temporomandibular Disorders. The sample comprised class II skeletal patients referred to an Oral and Maxillofacial Surgery center in the Brazilian Northeast. Seventy-three subjects were enrolled and completed the data collection, which consisted of a physical examination according to Axis I of the Research Diagnostic Criteria for Temporomandibular Disorders and facial analysis. Women represented 82.2% of the sample. Among the assessed subjects, 68.5% were already undergoing orthodontic treatment, and the mean overjet of patients was 6.97 mm. The prevalence of TMD in this sample was 46.6%, with muscular disorders being the most common. Patients with an anteroposterior discrepancy greater than 7 mm showed a higher occurrence of TMD (p = 0.017). This study demonstrated a high prevalence of TMD in skeletal class II patients referred for orthognathic surgery, especially in those with a pronounced overjet, being Group I (muscular disorders) and Group III (degenerative disorders) the most prevalent.

Outcome Evaluation of Reconstructive Septorhinoplasty in Patients with a History of Nasal Skin Necrosis.

Background: Nasal skin necrosis represents a severe rhinoplasty complication, potentially leading to nasal deformity and psychological distress. Objectives: To assess the safety, nasal function enhancement, and esthetic satisfaction in individuals with a history of nasal skin necrosis following reconstructive septorhinoplasty using patient-reported outcome measures. Methods: The case series included patients who underwent reconstructive septorhinoplasty 1 year after experiencing skin necrosis. Skeletal reconstruction was performed with an open approach using various autografts and techniques. The data collected encompassed demographics, medical history, operative reports, and postoperative events. The Standardized Cosmesis and Health Nasal Outcomes Survey (SCHNOS) was administered preoperatively and 1 year postoperatively. Results: Most patients were female (35/38, 92%), with median age of 33.5 years (17-57) and 2 prior rhinoplasties (1-5) by 1 surgeon (1-2). The follow-up median was 23.78 months (11.83-54.23). Necrosis predominantly affected the nasal tip and soft tissue triangles. Septorhinoplasty utilized 11 V-shaped columellar base and 27 mid-columella incisions, lasting on average 274 min (175-405). Septal grafts were adequate for reconstruction in only three cases. Autografts for reconstruction encompassed auricular cartilage (31), rib cartilage (22), temporal fascia (14), and rectus fascia (6). SCHNOS scores improved significantly, with the obstruction domain decreasing from a preoperative mean of 75.92 (±22.83) to 34.08 (±27.87) and the cosmesis domain from 85.43 (±19.06) to 39.73 (±23.77). No postoperative severe vascular complication occurred. More previous surgeries linked to decreased patient satisfaction. Conclusion: Reconstructive septorhinoplasty can be successful in restoring relatively normal nasal contour following prior soft tissue necrosis, with limited risk for complications.

Development of PainFace software to simplify, standardize, and scale up mouse grimace analyses.

Facial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace ( http://painface.net ) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. Platform accuracy was validated in 2 different laboratories, with 3 conditions that evoke grimacing-laparotomy surgery, bilateral hindpaw injection of carrageenan, and intraplantar injection of formalin. PainFace can generate up to 1 grimace score per second from a standard 30 frames/s video, making it possible to quantify facial grimacing over time, and operates at a speed that scales with computing power. By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.

Associations between facial expressions and observational pain in residents with dementia and chronic pain.

To identify specific facial expressions associated with pain behaviors using the PainChek application in residents with dementia. This is a secondary analysis from a study exploring the feasibility of PainChek to evaluate the effectiveness of a social robot (PARO) intervention on pain for residents with dementia from June to November 2021. Participants experienced PARO individually five days per week for 15 min (once or twice) per day for three consecutive weeks. The PainChek app assessed each resident's pain levels before and after each session. The association between nine facial expressions and the adjusted PainChek scores was analyzed using a linear mixed model. A total of 1820 assessments were completed with 46 residents. Six facial expressions were significantly associated with a higher adjusted PainChek score. Horizontal mouth stretch showed the strongest association with the score, followed by brow lowering parting lips, wrinkling of the nose, raising of the upper lip and closing eyes. However, the presence of cheek raising, tightening of eyelids and pulling at the corner lip were not significantly associated with the score. Limitations of using the PainChek app were identified. Six specific facial expressions were associated with observational pain scores in residents with dementia. Results indicate that automated real-time facial analysis is a promising approach to assessing pain in people with dementia. However, it requires further validation by human observers before it can be used for decision-making in clinical practice. Pain is common in people with dementia, while assessing pain is challenging in this group. This study generated new evidence of facial expressions of pain in residents with dementia. Results will inform the development of valid artificial intelligence-based algorithms that will support healthcare professionals in identifying pain in people with dementia in clinical situations. The study adheres to the CONSORT reporting guidelines. One resident with dementia and two family members of people with dementia were consulted and involved in the study design, where they provided advice on the protocol, information sheets and consent forms, and offered valuable insights to ensure research quality and relevance. Australian and New Zealand Clinical Trials Registry number (ACTRN12621000837820).

Skeletal reconstruction of fossil vertebrates as a process of hypothesis testing and a source of anatomical and palaeobiological inferences

Reconstructions of extinct animals play an important role in vertebrate palaeontology. Such reconstructions represent visual hypotheses regarding the original morphology of the vertebrates they depict, which are amenable to future testing as additional information comes to light through discoveries of new specimens and re-examination of specimens that have already been collected. In this contribution, we argue that the scientific value of reconstructing a fossil vertebrate extends beyond simple presentation of a visual hypothesis, because the process of creating a reconstruction is itself analytical and hypothetico-deductive. Successive drafts of the reconstruction represent provisional visual hypotheses that can be tested on the basis of their internal consistency and their congruence with empirical evidence about the extinct taxon that is the reconstruction’s subject. Iterative refinement of the reconstruction over successive rounds of testing and modification is likely to lead to discoveries about the subject’s anatomy, as certain anatomical possibilities are rejected and others found to be plausible. These anatomical discoveries, here termed first-order inferences, may in turn lead to second-order inferences about functional morphology or other aspects of palaeobiology. Three case studies from dinosaur palaeontology, respectively involving the skull of the hadrosaurid Edmontosaurus Lambe, 1917, the forelimb of the ceratopsid Pachyrhinosaurus Sternberg, 1950, and the hindlimb of an indeterminate ceratopsid, are provided to illustrate how the process of reconstruction can be a fertile source of discoveries.

MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures

Motion tracking systems based on optical sensors typically suffer from poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns. More recently, radiofrequency (RF) based approaches using commercial WiFi devices have emerged which offer low-cost ubiquitous sensing whilst preservin privacy. However, RF sensing systems typically output range-Doppler maps, time-frequency spectrograms, cross-range plots etc which cannot represent human motion intuitively and usually requires further processing. In this study, we propose MDPose, a novel framework for human skeletal motion reconstruction base on WiFi micro-Doppler signatures. MDPose provides an effective solution to represent human activity by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way. Specifically, MDPose is implemented over three sequential stage to address a series of challenges: First, a denoising algorithm is employed to remove any unwanted noise that may affect feature extraction and enhance weak Doppler measurements. Secondly, a convolutional neural network (CNN)-recurrent neural network (RNN) architecture is applied to learn temporal spatial dependenc from clean micro-Doppler signatures and restore velocity information to key points under the supervision of the motion capture (Mocap) system. Finally, a pose optimisation mechanism based on learning optimisation vectors is employed to estimate the initial state of the skeleton and to limit additional errors. We hav conducted a comprehensive set of tests in a variety of environments using numerous subjects with a single receiver radar system to demonstrate the performance of MDPose, and report 29.4mm mean absolute error over all key points positions on several common daily activities, which has performance comparable to that of state-ofthe- art RF-based pose estimation systems.

Prior-Guided Attribution of Deep Neural Networks for Obstetrics and Gynecology.

Obstetrics and gynecology (OB/GYN) are areas of medicine that specialize in the care of women during pregnancy and childbirth and in the diagnosis of diseases of the female reproductive system. Ultrasound scanning has become ubiquitous in these branches of medicine, as breast or fetal ultrasound images can lead the sonographer and guide him through his diagnosis. However, ultrasound scan images require a lot of resources to annotate and are often unavailable for training purposes because of confidentiality reasons, which explains why deep learning methods are still not as commonly used to solve OB/GYN tasks as in other computer vision tasks. In order to tackle this lack of data for training deep neural networks in this context, we propose Prior-Guided Attribution (PGA), a novel method that takes advantage of prior spatial information during training by guiding part of its attribution towards these salient areas. Furthermore, we introduce a novel prior allocation strategy method to take into account several spatial priors at the same time while providing the model enough degrees of liberty to learn relevant features by itself. The proposed method only uses the additional information during training, without needing it during inference. After validating the different elements of the method as well as its genericity on a facial analysis problem, we demonstrate that the proposed PGA method constantly outperforms existing baselines on two ultrasound imaging OB/GYN tasks: breast cancer detection and scan plane detection with segmentation prior maps.

Influence of upper lip inclination on facial profile attractiveness: A perceptive analysis.

One of the major objectives of orthodontic treatment is to improve facial attractiveness. To orthodontists, this means that the nose, lips, and chin form an attractive outline when the faces are viewed from one side. The aim of the study is to evaluate the influence of lip inclination on perceived attractiveness using North Indian observer ratings on profile silhouettes. A profile photograph of an Indian male and female were converted to silhouettes for the study using computer software. The lip inclination of the idealized profile image was altered in two-degree increments to create a series of profiles with different upper lip inclination angles and rated by laypersons on a Likert scale. An angle of 79° was deemed to be neither attractive nor unattractive. Therefore, within the range of 65°-75°, the pictures were deemed acceptable. Angles above or below this range, up to 91° were perceived as slightly unattractive, and anything outside the range of 93°-105° was deemed very unattractive. The study is not generalizable to ethnic groups other than the North Indian population. Another limitation is the inability to visualize the entire face for a complete facial analysis. An upper lip inclination of 75°-85° in the male profile was associated with perceived attractiveness in all three groups whereas it was in the range of 65°-75° for a female profile. An upper lip inclination angle ranging from 95° to 105° was associated with unattractiveness in all three groups of observers and requires correctional intervention.

Deep learning-based Facial Micro-Expression Analysis: A Survey of Datasets and Algorithms

Deep learning-based Facial Micro-Expression Analysis: A Survey of Datasets and Algorithms

Evaluation and Comparison of Facial Appearance Using the Golden Ratio: An Anthropometric Study in Preschool and School-Going Children of Santhal Tribe in West Bengal.

Introduction Golden ratio and beauty are two inseparable sides of the same coin and have been studied for centuries by the Greeks. This divine ratio is defined as an invincible parameter in aesthetic dentistry to measure looks, symmetry, and balance. Being beautiful and handsome also boosts confidence in today's children and therefore is a top priority for young growing kids. However, there is no study done to define facial measurements based on the golden ratio in preschool and school-going children that can succor aesthetics in formative years. The purpose of this research was to evaluate facial proportions in the vertical dimension, quantify them in reference to the golden ratio, and analyze the association with gender among preschool and school-going children of the Santhal tribe in the Bankura district. Materials and methods A total of 399 subjects, 198 children of 3-5 years with primary teeth and 201 children of 6-12 years with mixed dentition, were selected from schools in villages of Bankura district, West Bengal, India. The subjects were made to relax in a sitting position and a digital vernier caliper was used to record the following vertical facial parameters: total facial height (TFH), trichion-gnathion distance (Tr-Gn), and subnasale-gnathion distance (Sn-Gn). The total facial height was correlated with sexual dimorphism and dentition. The ratio of Tr-Sn/Sn-Gn was calculated and compared with the golden ratio. The ratio was classified as normal (if it was between 1.6 and 1.699, i.e., normal to the golden ratio value), long (if it was more than 1.699, i.e., more than the golden ratio value), or short (if it was below 1.6, i.e., less than the golden ratio value). This facial analysis based on the golden ratio was correlated to sexual dimorphism and dentition. The data were recorded, compared with gender, and statistically analyzed using the unpaired t-test and Chi-square test. Results The total facial height was larger in males than females in both primary and mixed dentition; however, the value was highly significant in primary dentition. Tr-Sn/Sn-Gn ratios were lower in the longfacial categoryin both males and females in both preschool and school-going children. The values were statistically significant in these ratios for both dentitions. Conclusion The majority of children in the Santhal tribe of Bankura in West Bengal did not conform to the golden ratio and showed long faces. There was a significant association of facial features with sexual dimorphism. Clinical significance The early prediction of facial features in children and its confirmation with the established golden ratio can be considered an imperative parameter to comprehend facial aesthetics and symmetry.

Investigation of stratum corneum cell morphology and content using novel machine-learning image analysis.

The morphology and content of stratum corneum (SC) cells provide information on the physiological condition of the skin. Although the morphological and biochemical properties of the SC are known, no method is available to fully access and interpret this information. This study aimed to develop a method to comprehensively decode the physiological information of the skin, based on the SC. Therefore, we established a novel image analysis technique based on artificial intelligence (AI) and multivariate analysis to predict skin conditions. SC samples were collected from participants, imaged, and annotated. Nine biomarkers were measured in the samples using enzyme-linked immunosorbent assay. The data were then used to teach machine-learning models to recognize individual SC cell regions and estimate the levels of the nine biomarkers from the images. Skin physiological indicators (e.g., skin barrier function, facial analysis, and questionnaires) were measured or obtained from the participants. Multivariate analysis, including biomarker levels ​​and structural parameters of the SC as variables, was used to estimate these physiological indicators. We established two machine-learning models. The accuracy of recognition was assessed according to the average intersection over union (0.613), precision (0.953), recall (0.640), and F-value (0.766). The predicted biomarker levels significantly correlated with the measured levels. Skin physiological indicators and questionnaire answers were predicted with strong correlations and correct answer rates. Various physiological skin conditions can be predicted from images of the SC using AI models and multivariate analysis. Our method is expected to be useful for dermatological treatment optimization.

Forensic facial reconstruction: an anthropometric study of the ear

ABSTRACT The recognition of an individual, supported by forensic facial reconstruction (FFR), can be established using several anatomical references, such as soft tissue thickness, position, and size of the eyes, nose, lips, and ears. The inclusion of additional visual inputs to the process, such as the ear, can refine an individual’s appearance. However, to the best of our knowledge, existing methods are scarce and undertested. To improve recognition, this study aimed to verify the size, position, and inclination of the ears, and their possible relationship with the skull. Ninety-nine research participants (55 females and 44 males) aged between 18 and 65 years were evaluated. Direct (on research participants) and indirect (photographs) measurements were performed using imaging software. Eighteen relationships were tested to determine their sizes, inclinations, and positions with respect to other cranial structures. It can be concluded that considering both length and width, the male ear is larger than the female ear, and based on the angle between the superaurale and otobasion inferius line and the Frankfurt plane, the ear inclination is 73º for both sexes. In addition, the upper portion of the ear coincides with the eyebrows. These references can help determine the characteristics of the ear during FFR.

Multimodal emotion recognition based on the fusion of vision, EEG, ECG, and EMG signals

This paper presents a novel approach for emotion recognition (ER) based on Electroencephalogram (EEG), Electromyogram (EMG), Electrocardiogram (ECG), and computer vision. The proposed system includes two different models for physiological signals and facial expressions deployed in a real-time embedded system. A custom dataset for EEG, ECG, EMG, and facial expression was collected from 10 participants using an Affective Video Response System. Time, frequency, and wavelet domain-specific features were extracted and optimized, based on their Visualizations from Exploratory Data Analysis (EDA) and Principal Component Analysis (PCA). Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Histogram of Oriented Gradients (HOG), and Gabor descriptors were used for differentiating facial emotions. Classification models, namely decision tree, random forest, and optimized variants thereof, were trained using these features. The optimized Random Forest model achieved an accuracy of 84%, while the optimized Decision Tree achieved 76% for the physiological signal-based model. The facial emotion recognition (FER) model attained an accuracy of 84.6%, 74.3%, 67%, and 64.5% using K-Nearest Neighbors (KNN), Random Forest, Decision Tree, and XGBoost, respectively. Performance metrics, including Area Under Curve (AUC), F1 score, and Receiver Operating Characteristic Curve (ROC), were computed to evaluate the models. The outcome of both results, i.e., the fusion of bio-signals and facial emotion analysis, is given to a voting classifier to get the final emotion. A comprehensive report is generated using the Generative Pretrained Transformer (GPT) language model based on the resultant emotion, achieving an accuracy of 87.5%. The model was implemented and deployed on a Jetson Nano. The results show its relevance to ER. It has applications in enhancing prosthetic systems and other medical fields such as psychological therapy, rehabilitation, assisting individuals with neurological disorders, mental health monitoring, and biometric security.

Analysis of Sexual Dimorphic Features of the Jawline and Chin in White Celebrity Faces.

The jawline greatly influences facial shape and contributes to facial dimorphism. Analysis of lower facial shape and sexual dimorphic differences in contemporary attractive white faces can advance the goals of lower facial aesthetics and facial gender-affirming surgery (FGAS). Full-face, front-view photos of 47 white female and 21 white male celebrities were included from a list generated using GQ magazine's Highest Paid Models issue, People Magazine's Beautiful issue (1991-2022), and celebrities featured on lifestyle websites. Facial landmarks were detected through a facial analysis program using Vision framework and MATLAB. After converting pixel distances to absolute distances, lower face measurements were compared between males and females. The mean lower facial height was 6.08cm in females and 7.00cm in males (P value<0.001). The mean bigonial width was 11.21cm in females and 12.30cm in males (P value<0.001). The ratio of facial height to lower facial height was 2.98 in females and 2.76 in males (P value<0.001), signifying that symmetry in facial thirds is more prevalent in attractive female faces, while a longer lower face is more common in attractive male faces. The greatest differences in female and male facial contours were at gonial angles and chin. Analysis of contemporary white celebrity faces demonstrated significantly wider and longer lower facial measurements in males. The overall contour of the female lower face was more tapered at the gonial angles and chin compared to males. These results are important when planning lower facial rejuvenation or FGAS, as lower face size and proportions influence perceived gender and attractiveness.

Tensile Stress-Activated and Exosome-Transferred YAP/TAZ-Notch Circuit Specifies Type H Endothelial Cell for Segmental Bone Regeneration.

The Ilizarov technique has been continuously innovated to utilize tensile stress (TS) for inducing a bone development-like regenerative process, aiming to achieve skeletal elongation and reconstruction. However, it remains uncertain whether this distraction osteogenesis (DO) process induced by TS involves the pivotal coupling of angiogenesis and osteogenesis mediated by type H endothelial cells (THECs). In this study, it is demonstrated that the Ilizarov technique induces the formation of a metaphysis-like architecture composed of THECs, leading to segmental bone regeneration during the DO process. Mechanistically, cell-matrix interactions-mediated activation of yes-associated protein (YAP)/transcriptional co-activator with PDZ-binding motif (TAZ) transcriptionally upregulates the expression of Notch1 and Delta-like ligand 4, which act as direct positive regulators of THECs phenotype, in bone marrow endothelial cells (BMECs) upon TS stimulation. Simultaneously, the Notch intracellular domain enhances YAP/TAZ activity by transcriptionally upregulating YAP expression and stabilizing TAZ protein, thus establishing the YAP/TAZ-Notch circuit. Additionally, TS-stimulated BMECs secrete exosomes enriched with vital molecules in this positive feedback pathway, which can be utilized to promote segmental bone defect healing, mimicking the therapeutic effects of Ilizarov technique. The findings advance the understanding of TS-induced segmental bone regeneration and establish the foundation for innovative biological therapeutic strategies aimed at activating THECs.

CMF defects database: A craniomaxillofacial defects dataset and a data-driven repair method

Due to the large number of medical images generated each year, data-driven machine learning methods hold great promise in the field of medical image diagnosis and analysis. In recent years, the application of deep neural networks in oral and cranio-maxillofacial surgery has gradually increased, helping surgeons to obtain more comprehensive information and develop more accurate treatment planning, including segmentation and recognition of anatomical structures, disease diagnosis, and automatic surgical planning. This paper presents a new dataset of maxillofacial defect images, containing computed tomography (CT) data of 125 normal human cranial skeletal 3D reconstructions and 2625 corresponding craniomaxillofacial bone defects. The data are classified according to seven different types of defects which are anterior skull and forehead, zygomatic arch, zygomatic bone, Naso-orbital-ethmoidal (NOE), wall of the maxillary sinus, mandibular body, mandibular ramus, and condylar respectively. Each kind of defect is further divided into three categories according to the different locations, including left side, right side, and bilateral sides (or defect passes through the midline of the face). There are three main contributions: first, a dataset is publicly available to assist researchers in the development of data-based methods for generating missing portions of the skull; second, a repair method is proposed to evaluate the dataset; third, a review of various related datasets. The release of this dataset aims to advance artificial intelligence methods in the field of oral and cranio-maxillofacial surgery and plastic surgery helping physicians to reduce the difficulty and time consumption while improving the efficiency and accuracy of prosthesis design.

AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

In the age of artificial intelligence (AI), technological progress is changing established workflows and enabling some basic routines to be updated. In dentistry, the patient’s face is a crucial part of treatment planning, although it has always been difficult to grasp in an analytical way. This review highlights the current digital advances that, thanks to AI tools, allow us to implement facial features beyond symmetry and proportionality and incorporate facial analysis into diagnosis and treatment planning in orthodontics. A Scopus literature search was conducted to identify the topics with the greatest research potential within digital orthodontics over the last five years. The most researched and cited topic was artificial intelligence and its applications in orthodontics. Apart from automated 2D or 3D cephalometric analysis, AI finds its application in facial analysis, decision-making algorithms as well as in the evaluation of treatment progress and retention. Together with AI, other digital advances are shaping the face of today’s orthodontics. Without any doubts, the era of “old” orthodontics is at its end, and modern, face-driven orthodontics is on the way to becoming a reality in modern orthodontic practices.

Utilization of Facial PPG Signals as a Novel Source for Blood Pressure Estimation

Hypertension, a prevalent global health concern, necessitates accurate and non-invasive blood pressure estimation techniques for effective monitoring and management. This paper proposes a novel machine learning approach utilizing Photo plethysmography (PPG) signals for precise blood pressure estimation. PPG signals, obtained conveniently through wearable devices, offer valuable physiological information related to cardiovascular activity. Leveraging advanced machine learning algorithms, including deep learning architectures and feature extraction methods, our proposed technique aims to establish a robust model for blood pressure estimation using facial image analysis. The methodology involves preprocessing PPG signals, extracting relevant features, and employing sophisticated machine learning models for regression analysis. The evaluation of this novel approach involves comprehensive experimentation with diverse datasets, ensuring its efficacy across various demographic groups and conditions. Results demonstrate promising accuracy and reliability in estimating blood pressure values, suggesting the potential for practical implementation in healthcare settings. The proposed technique showcases a promising avenue for non-invasive and accessible blood pressure monitoring, contributing significantly to personalized healthcare and continuous health monitoring systems.