Deepfake detection by human crowds, machines, and machine-informed crowds.

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model's prediction are more accurate than either alone, but inaccurate model predictions often decrease participants' accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants' performance while mostly not affecting the model's performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems.

Reliably discerning real human faces from fake ones, known as antispoofing, is crucial for facial recognition systems. While neuromorphic systems offer integrated sensing-memory-processing functions, they still struggle with efficient antispoofing techniques. Here we introduce a neuromorphic facial recognition system incorporating multidimensional deep ultraviolet (DUV) optoelectronic synapses to address these challenges. To overcome the complexity and high cost of producing DUV synapses using traditional wide-bandgap semiconductors, we developed a low-temperature (≤70 °C) solution process for fabricating DUV synapses based on PEA2PbBr4/C8-BTBT heterojunction field-effect transistors. This method enables the large-scale (4-in.), uniform, and transparent production of DUV synapses. These devices respond to both DUV and visible light, showing multidimensional features. Leveraging the unique ability of the multidimensional DUV synapse (MDUVS) to discriminate real human skin from artificial materials, we have achieved robust neuromorphic facial recognition with antispoofing capability, successfully identifying genuine human faces with an accuracy exceeding 92%.

Emotional processing impairments in patients with insula lesions following stroke.

Functional imaging has helped to understand the role of the human insula as a major processing network for integrating input with the current state of the body. However, these studies remain at a correlative level. Studies that have examined insula damage show lesion-specific performance deficits. Case reports have provided anecdotal evidence for deficits following insula damage, but group lesion studies offer a number of advances in providing evidence for functional representation of the insula. We conducted a systematic literature search to review group studies of patients with insula damage after stroke and identified 23 studies that tested emotional processing performance in these patients. Eight of these studies assessed emotional processing of visual (most commonly IAPS), auditory (e.g., prosody), somatosensory (emotional touch) and autonomic function (heart rate variability). Fifteen other studies looked at social processing, including emotional face recognition, gaming tasks and tests of empathy. Overall, there was a bias towards testing only patients with right-hemispheric lesions, making it difficult to consider hemisphere specificity. Although many studies included an overlay of lesion maps to characterise their patients, most did not differentiate lesion statistics between insula subunits and/or applied voxel-based associations between lesion location and impairment. This is probably due to small group sizes, which limit statistical comparisons. We conclude that multicentre analyses of lesion studies with comparable patients and performance tests are needed to definitively test the specific function of parts of the insula in emotional processing and social interaction.

Assessing inclusion and representativeness on digital platforms for health education: Evidence from YouTube.

BACKGROUND: Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube. METHODS: Leveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video's average daily view count. A video that generates a higher view count is considered to be more popular. RESULTS: The voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78%, AUC = 76%), while the gender and race predictions use facial recognition (accuracy = 93%, AUC = 92% and accuracy = 82%, AUC = 80%, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non - white group have high view counts. CONCLUSION: Presenters' demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.

Optimization and evaluation of facial recognition models for Williams-Beuren syndrome.

Williams-Beuren syndrome (WBS) is a rare genetic disorder characterized by special facial gestalt, delayed development, and supravalvular aortic stenosis or/and stenosis of the branches of the pulmonary artery. We aim to develop and optimize accurate models of facial recognition to assist in the diagnosis of WBS, and to evaluate their effectiveness by using both five-fold cross-validation and an external test set. We used a total of 954 images from 135 patients with WBS, 124 patients suffering from other genetic disorders, and 183 healthy children. The training set comprised 852 images of 104 WBS cases, 91 cases of other genetic disorders, and 145 healthy children from September 2017 to December 2021 at the Guangdong Provincial People's Hospital. We constructed six binary classification models of facial recognition for WBS by using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. Transfer learning was used to pre-train the models, and each model was modified with a variable cosine learning rate. Each model was first evaluated by using five-fold cross-validation and then assessed on the external test set. The latter contained 102 images of 31 children suffering from WBS, 33 children with other genetic disorders, and 38 healthy children. To compare the capabilities of these models of recognition with those of human experts in terms of identifying cases of WBS, we recruited two pediatricians, a pediatric cardiologist, and a pediatric geneticist to identify the WBS patients based solely on their facial images. We constructed six models of facial recognition for diagnosing WBS using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. The model based on VGG-19BN achieved the best performance in terms of five-fold cross-validation, with an accuracy of 93.74% ± 3.18%, precision of 94.93% ± 4.53%, specificity of 96.10% ± 4.30%, and F1 score of 91.65% ± 4.28%, while the VGG-16BN model achieved the highest recall value of 91.63% ± 5.96%. The VGG-19BN model also achieved the best performance on the external test set, with an accuracy of 95.10%, precision of 100%, recall of 83.87%, specificity of 93.42%, and F1 score of 91.23%. The best performance by human experts on the external test set yielded values of accuracy, precision, recall, specificity, and F1 scores of 77.45%, 60.53%, 77.42%, 83.10%, and 66.67%, respectively. The F1 score of each human expert was lower than those of the EfficientNet-b3 (84.21%), ResNet-50 (74.51%), VGG-16 (85.71%), VGG-16BN (85.71%), VGG-19 (83.02%), and VGG-19BN (91.23%) models. CONCLUSION: The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate. WHAT IS KNOWN: • The facial gestalt of WBS, often described as "elfin," includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS. WHAT IS NEW: • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.

Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.

BACKGROUND: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. OBJECTIVES: This study develops advanced models to enhance the accuracy of diagnosis of NS. METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. CONCLUSION: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.

Facial recognition models for identifying genetic syndromes associated with pulmonary stenosis in children.

BACKGROUND: Williams-Beuren syndrome, Noonan syndrome, and Alagille syndrome are common types of genetic syndromes (GSs) characterized by distinct facial features, pulmonary stenosis, and delayed growth. In clinical practice, differentiating these three GSs remains a challenge. Facial gestalts serve as a diagnostic tool for recognizing Williams-Beuren syndrome, Noonan syndrome, and Alagille syndrome. Pretrained foundation models (PFMs) can be considered the foundation for small-scale tasks. By pretraining with a foundation model, we propose facial recognition models for identifying these syndromes. METHODS: A total of 3297 (n = 1666) facial photos were obtained from children diagnosed with Williams-Beuren syndrome (n = 174), Noonan syndrome (n = 235), and Alagille syndrome (n = 51), and from children without GSs (n = 1206). The photos were randomly divided into five subsets, with each syndrome and non-GS equally and randomly distributed in each subset. The proportion of the training set and the test set was 4:1. The ResNet-100 architecture was employed as the backbone model. By pretraining with a foundation model, we constructed two face recognition models: one utilizing the ArcFace loss function, and the other employing the CosFace loss function. Additionally, we developed two models using the same architecture and loss function but without pretraining. The accuracy, precision, recall, and F1 score of each model were evaluated. Finally, we compared the performance of the facial recognition models to that of five pediatricians. RESULTS: Among the four models, ResNet-100 with a PFM and CosFace loss function achieved the best accuracy (84.8%). Of the same loss function, the performance of the PFMs significantly improved (from 78.5% to 84.5% for the ArcFace loss function, and from 79.8% to 84.8% for the CosFace loss function). With and without the PFM, the performance of the CosFace loss function models was similar to that of the ArcFace loss function models (79.8% vs 78.5% without PFM; 84.8% vs 84.5% with PFM). Among the five pediatricians, the highest accuracy (0.700) was achieved by the senior-most pediatrician with genetics training. The accuracy and F1 scores of the pediatricians were generally lower than those of the models. CONCLUSIONS: A facial recognition-based model has the potential to improve the identification of three common GSs with pulmonary stenosis. PFMs might be valuable for building screening models for facial recognition. Key messages What is already known on this topic:  Early identification of genetic syndromes (GSs) is crucial for the management and prognosis of children with pulmonary stenosis (PS). Facial phenotyping with convolutional neural networks (CNNs) often requires large-scale training data, limiting its usefulness for GSs. What this study adds:  We successfully built multi-classification models based on face recognition using a CNN to accurately identify three common PS-associated GSs. ResNet-100 with a pretrained foundation model (PFM) and CosFace loss function achieved the best accuracy (84.8%). Pretrained with the foundation model, the performance of the models significantly improved, although the impact of the type of loss function appeared to be minimal. How this study might affect research, practice, or policy:  A facial recognition-based model has the potential to improve the identification of GSs in children with PS. The PFM might be valuable for building identification models for facial detection.

Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study.

BACKGROUND: While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. OBJECTIVE: We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. METHODS: Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. RESULTS: DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score's levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. CONCLUSIONS: If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.

The lesser of two evils: Assessing the public acceptance of AI thermal facial recognition during the COVID-19 crisis.

AI thermal facial recognition (AITFR) has been rapidly applied globally in the fight against Coronavirus disease 2019 (COVID-19). However, AITFR has also been accompanied by a controversy regarding whether the public accepts it. Therefore, it is necessary to assess the acceptance of AITFR during the COVID-19 crisis. Drawing upon the theory of acceptable risk and Siegrist's causal model of public acceptance (PA), we built a combined psychological model that included the perceived severity of COVID-19 (PSC) to describe the influencing factors and pathways of AITFR acceptance. This model was verified through a survey conducted in Xi'an City, Shaanxi Province, China, which collected 754 valid questionnaires. The results show that (1) COVID-19 provides various application scenarios for AI-related technologies. However, the respondents' trust in AITFR was found to be very low. Additionally, the public appeared concerned about the privacy disclosure issue and the accuracy of the AITFR algorithm. (2) The PSC, social trust (ST), and perceived benefit (PB) were found to directly affect AITFR acceptance. (3) The PSC was found to have a significant positive effect on perceived risk (PR). PR was found to have no significant effect on PA, which is inconsistent with the findings of previous studies. (4) The PB were found to be a stronger mediator of the indirect effect of the PSC on ST induced by AITFR acceptance.

The Neural Mechanism Underlying Differentiated In-Group Versus Out-Group Face Recognition and Memory, Identification, Empathy and Pro-social Behavior: Evidence from fMRI and ERP Studies.

In the context of perceiving individuals within and outside of social groups, there are distinct cognitive processes and mechanisms in the brain. Extensive research in recent years has delved into the neural mechanisms that underlie differences in how we perceive individuals from different social groups. To gain a deeper understanding of these neural mechanisms, we present a comprehensive review from the perspectives of facial recognition and memory, intergroup identification, empathy, and pro-social behavior. Specifically, we focus on studies that utilize functional magnetic resonance imaging (fMRI) and event-related potential (ERP) techniques to explore the relationship between brain regions and behavior. Findings from fMRI studies reveal that the brain regions associated with intergroup differentiation in perception and behavior do not operate independently but instead exhibit dynamic interactions. Similarly, ERP studies indicate that the amplitude of neural responses shows various combinations in relation to perception and behavior.

Facing up to Problem Gambling: Tracing the Emergence of Facial Recognition Technology as a means of Enforcing Voluntary Self-Exclusion.

Computer technology has long been touted as a means of increasing the effectiveness of voluntary self-exclusion schemes - especially in terms of relieving gaming venue staff of the task of manually identifying and verifying the status of new customers. This paper reports on the government-led implementation of facial recognition technology as part of an automated self-exclusion program in the city of Adelaide in South Australia-one of the first jurisdiction-wide enforcements of this controversial technology in small venue gambling. Drawing on stakeholder interviews, site visits and documentary analysis over a two year period, the paper contrasts initial claims that facial recognition offered a straightforward and benign improvement to the efficiency of the city's long-running self-excluded gambler program, with subsequent concerns that the new technology was associated with heightened inconsistencies, inefficiencies and uncertainties. As such, the paper contends that regardless of the enthusiasms of government, tech industry and gaming lobby, facial recognition does not offer a ready 'technical fix' to problem gambling. The South Australian case illustrates how this technology does not appear to better address the core issues underpinning problem gambling, and/or substantially improve conditions for problem gamblers to refrain from gambling. As such, it is concluded that the gambling sector needs to pay close attention to the practical outcomes arising from initial cases such as this, and resist industry pressures for the wider replication of this technology in other jurisdictions.

Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study.

PURPOSE: This study aimed to assess whether an artificial intelligence model based on facial expressions can accurately predict significant postoperative pain. METHODS: A total of 155 facial expressions from patients who underwent gastric cancer surgery were analyzed to extract facial action units (AUs), gaze, landmarks, and positions. These features were used to construct various machine learning (ML) models, designed to predict significant postoperative pain intensity (NRS ≥ 7) from less significant pain (NRS < 7). Significant AUs predictive of NRS ≥ 7 were determined and compared to AUs known to be associated with pain in awake patients. The area under the receiver operating characteristic curves (AUROCs) of the ML models was calculated and compared using DeLong's test. RESULTS: AU17 (chin raising) and AU20 (lip stretching) were found to be associated with NRS ≥ 7 (both P ≤ 0.004). AUs known to be associated with pain in awake patients did not show an association with pain in postoperative patients. An ML model based on AU17 and AU20 demonstrated an AUROC of 0.62 for NRS ≥ 7, which was inferior to a model based on all AUs (AUROC = 0.81, P = 0.006). Among facial features, head position and facial landmarks proved to be better predictors of NRS ≥ 7 (AUROC, 0.85-0.96) than AUs. A merged ML model that utilized gaze and eye landmarks, as well as head position and facial landmarks, exhibited the best performance (AUROC, 0.90) in predicting significant postoperative pain. CONCLUSION: ML models using facial expressions can accurately predict the presence of significant postoperative pain and have the potential to screen patients in need of rescue analgesia. TRIAL REGISTRATION NUMBER: This study was registered at ClinicalTrials.gov (NCT05477303; date: June 17, 2022).

Privacy, bias and the clinical use of facial recognition technology: A survey of genetics professionals.

Facial recognition technology (FRT) has been adopted as a precision medicine tool. The medical genetics field highlights both the clinical potential and privacy risks of this technology, putting the discipline at the forefront of a new digital privacy debate. Investigating how geneticists perceive the privacy concerns surrounding FRT can help shape the evolution and regulation of the field, and provide lessons for medicine and research more broadly. Five hundred and sixty-two genetics clinicians and researchers were approached to fill out a survey, 105 responded, and 80% of these completed. The survey consisted of 48 questions covering demographics, relationship to new technologies, views on privacy, views on FRT, and views on regulation. Genetics professionals generally placed a high value on privacy, although specific views differed, were context-specific, and covaried with demographic factors. Most respondents (88%) agreed that privacy is a basic human right, but only 37% placed greater weight on it than other values such as freedom of speech. Most respondents (80%) supported FRT use in genetics, but not necessarily for broader clinical use. A sizeable percentage (39%) were unaware of FRT's lower accuracy rates in marginalized communities and of the mental health effects of privacy violations (62%), but most (76% and 75%, respectively) expressed concern when informed. Overall, women and those who self-identified as politically progressive were more concerned about the lower accuracy rates in marginalized groups (88% vs. 64% and 83% vs. 63%, respectively). Younger geneticists were more wary than older geneticists about using FRT in genetics (28% compared to 56% "strongly" supported such use). There was an overall preference for more regulation, but respondents had low confidence in governments' or technology companies' ability to accomplish this. Privacy views are nuanced and context-dependent. Support for privacy was high but not absolute, and clear deficits existed in awareness of crucial FRT-related discrimination potential and mental health impacts. Education and professional guidelines may help to evolve views and practices within the field.

The Use of Facial Recognition Software and Published Manuscripts to Examine Trends in Surgical Editorial Board Diversity.

INTRODUCTION: Recent social justice movements have highlighted the need for improved diversity and inclusion. These movements have emphasized the need for inclusivity of all genders and races in all sectors including surgical editorial boards. There is currently not an established, standardized method to assess the gender, racial, and ethnic makeup of surgical editorial board rosters, yet artificial intelligence is a method that can be utilized to determine gender and race in an unbiased manner. The aim of the present study is to determine if recent social justice movements correlate with an increase in diversity-themed articles published and if there is an increase in the gender and racial makeup of surgical editorial boards determined by artificial intelligence software. METHODS: Impact factor was used to assess and rank highly regarded general surgery journals. The website of each of these journals was examined for pledges of diversity in their mission statements and core beliefs of conduct. To determine the number of diversity-themed articles that were published during 2016 and 2021, each surgical journal was analyzed for diversity-themed articles using 10 specific keywords in PubMed. To determine the racial and gender makeup of editorial boards in 2016 and 2021, we obtained the current and the 2016 editorial board roster. Roster member images were collected from academic institutional websites. Betaface facial recognition software was used to assess the images. The software assigned the gender, race and ethnicity of the image supplied. Betaface results were analyzed using a Chi Square Test of Independence. RESULTS: We analyzed 17 surgical journals. Only four of 17 journals were found to have diversity pledges on their website. For diversity themed publications, 1% of articles in 2016 and 2.7% in 2021 were published specifically about diversity. There was a significant increase in the amount of diversity articles/journal published per year in 2016 (6.59) compared to 2021 (25.94, P < 0.001). There was no correlation between impact factor and articles that publish diversity keywords. 1968 editorial board member images were analyzed using Betaface software to determine gender and race in both time periods. There was no significant increase in diversity of editorial board members regarding gender, race, and ethnicity temporally from 2016 to 2021. CONCLUSIONS: In the present study, we found that although the number of diversity-themed articles has increased over the last 5 y, however the gender and racial makeup of surgical editorial boards has not changed. Further initiatives are needed to better track and diversify the gender and racial composition of surgical editorial boards.

Fetal brain activity and the free energy principle.

OBJECTIVES: To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence. METHODS: In this observational study, we used a four-dimensional ultrasound technique obtained to collect images of fetal faces from pregnancies at 27-37 weeks of gestation, between February and December 2021. We developed an artificial intelligence classifier that recognizes fetal facial expressions, which are thought to relate to fetal brain activity. We then applied the classifier to video files of facial images to generate each expression category's probabilities. We calculated the chaotic dimensions from the probability lists, and we created and investigated the free energy principle's mathematical model that was assumed to be linked to the chaotic dimension. We used a Mann-Whitney test, linear regression test, and one-way analysis of variance for statistical analysis. RESULTS: The chaotic dimension revealed that the fetus had dense and sparse states of brain activity, which fluctuated at a statistically significant level. The chaotic dimension and free energy were larger in the sparse state than in the dense state. CONCLUSIONS: The fluctuating free energy suggests consciousness seemed to exist in the fetus after 27 weeks.

Remembering to whom we transmit information during pandemics: the effect of face masks on destination memory.

Considering the global pandemic we currently experience, face masks have become standard in our daily routine. Even though surgical masks are established as a safety measure against the dissemination of COVID-19, previous research showed that their wearing compromises face recognition. Consequently, the capacity to remember to whom we transmit information-destination memory-could also be compromised. In our study, through a between-participants design (experiment 1) and a within-participants design (experiment 2), undergraduate students have to transmit Portuguese proverbs to masked and unmasked celebrity faces. Following our hypothesis, participants who shared information with masked faces had worse destination memory performance than those who shared information with unmasked faces. Also, we observed lower recognition for masked faces compared to unmasked faces. These results were expected since using a surgical mask affects facial recognition, thus making it harder to recognize a person to whom information was previously transmitted. More importantly, these results also support the idea that variables associated with the recipient's face are important for destination memory performance.

Neuronetwork Approach in the Early Diagnosis of Depression.

BACKGROUND: Depression is a common mental illness, with around 280 million people suffering from depression worldwide. At present, the main way to quantify the severity of depression is through psychometric scales, which entail subjectivity on the part of both patient and clinician. In the last few years, deep (machine) learning is emerging as a more objective approach for measuring depression severity. We now investigate how neural networks might serve for the early diagnosis of depression. SUBJECTS AND METHODS: We searched Medline (Pubmed) for articles published up to June 1, 2023. The search term included Depression AND Diagnostics AND Artificial Intelligence. We did not search for depression studies of machine learning other than neural networks, and selected only those papers attesting to diagnosis or screening for depression. RESULTS: Fifty-four papers met our criteria, among which 14 using facial expression recordings, 14 using EEG, 5 using fMRI, and 5 using audio speech recording analysis, whereas 6 used multimodality approach, two were the text analysis studies, and 8 used other methods. CONCLUSIONS: Research methodologies include both audio and video recordings of clinical interviews, task performance, including their subsequent conversion into text, and resting state studies (EEG, MRI, fMRI). Convolutional neural networks (CNN), including 3D-CNN and 2D-CNN, can obtain diagnostic data from the videos of the facial area. Deep learning in relation to EEG signals is the most commonly used CNN. fMRI approaches use graph convolutional networks and 3D-CNN with voxel connectivity, whereas the text analyses use CNNs, including LSTM (long/short-term memory). Audio recordings are analyzed by a hybrid CNN and support vector machine model. Neural networks are used to analyze biomaterials, gait, polysomnography, ECG, data from wrist wearable devices, and present illness history records. Multimodality studies analyze the fusion of audio features with visual and textual features using LSTM and CNN architectures, a temporal convolutional network, or a recurrent neural network. The accuracy of different hybrid and multimodality models is 78-99%, relative to the standard clinical diagnoses.

De Novo and Inherited Pathogenic Variants in KDM3B Cause Intellectual Disability, Short Stature, and Facial Dysmorphism.

By using exome sequencing and a gene matching approach, we identified de novo and inherited pathogenic variants in KDM3B in 14 unrelated individuals and three affected parents with varying degrees of intellectual disability (ID) or developmental delay (DD) and short stature. The individuals share additional phenotypic features that include feeding difficulties in infancy, joint hypermobility, and characteristic facial features such as a wide mouth, a pointed chin, long ears, and a low columella. Notably, two individuals developed cancer, acute myeloid leukemia and Hodgkin lymphoma, in childhood. KDM3B encodes for a histone demethylase and is involved in H3K9 demethylation, a crucial part of chromatin modification required for transcriptional regulation. We identified missense and truncating variants, suggesting that KDM3B haploinsufficiency is the underlying mechanism for this syndrome. By using a hybrid facial-recognition model, we show that individuals with a pathogenic variant in KDM3B have a facial gestalt, and that they show significant facial similarity compared to control individuals with ID. In conclusion, pathogenic variants in KDM3B cause a syndrome characterized by ID, short stature, and facial dysmorphism.

Face-Information Sampling in Super-Recognizers.

Perceptual processes underlying individual differences in face-recognition ability remain poorly understood. We compared visual sampling of 37 adult super-recognizers-individuals with superior face-recognition ability-with that of 68 typical adult viewers by measuring gaze position as they learned and recognized unfamiliar faces. In both phases, participants viewed faces through "spotlight" apertures that varied in size, with face information restricted in real time around their point of fixation. We found higher accuracy in super-recognizers at all aperture sizes-showing that their superiority does not rely on global sampling of face information but is also evident when they are forced to adopt piecemeal sampling. Additionally, super-recognizers made more fixations, focused less on eye region, and distributed their gaze more than typical viewers. These differences were most apparent when learning faces and were consistent with trends we observed across the broader ability spectrum, suggesting that they are reflective of factors that vary dimensionally in the broader population.

Narcissistic individuals exhibit poor recognition memory.

Here, we examine face memory among individuals who are self-focused and care little about others' needs: grandiose narcissists. Given narcissistic individuals' excessive self-focus and tendency to disregard the needs of others, they may struggle to recognize faces and their surrounding environment. Indeed, narcissistic individuals demonstrated worse recognition memory than non-narcissistic individuals in recognition memory tests for faces (Studies 1 [N = 332] and 2 [N = 261]). This difference also occurred for nonsocial stimuli (i.e., objects, houses, cars), suggesting a broad recognition deficit (Study 3A [N = 178], 3B [N = 203], 3C [N = 274]). Narcissistic individuals' excessive self-focus predicted this memory deficit (Study 4 [N = 187]). Grandiose narcissism may therefore influence visual recognition memory, highlighting the potential for future research linking personality and cognitive performance.