Dataset Information

Identification of acutely sick people and facial cues of sickness.

ABSTRACT: Detection and avoidance of sick individuals have been proposed as essential components in a behavioural defence against disease, limiting the risk of contamination. However, almost no knowledge exists on whether humans can detect sick individuals, and if so by what cues. Here, we demonstrate that untrained people can identify sick individuals above chance level by looking at facial photos taken 2 h after injection with a bacterial stimulus inducing an immune response (2.0 ng kg-1 lipopolysaccharide) or placebo, the global sensitivity index being d' = 0.405. Signal detection analysis (receiver operating characteristic curve area) showed an area of 0.62 (95% confidence intervals 0.60-0.63). Acutely sick people were rated by naive observers as having paler lips and skin, a more swollen face, droopier corners of the mouth, more hanging eyelids, redder eyes, and less glossy and patchy skin, as well as appearing more tired. Our findings suggest that facial cues associated with the skin, mouth and eyes can aid in the detection of acutely sick and potentially contagious people.

SUBMITTER: Axelsson J

PROVIDER: S-EPMC5784201 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Identification of acutely sick people and facial cues of sickness.

Axelsson John J Sundelin Tina T Olsson Mats J MJ Sorjonen Kimmo K Axelsson Charlotte C Lasselin Julie J Lekander Mats M

Proceedings. Biological sciences 20180101 1870

Detection and avoidance of sick individuals have been proposed as essential components in a behavioural defence against disease, limiting the risk of contamination. However, almost no knowledge exists on whether humans can detect sick individuals, and if so by what cues. Here, we demonstrate that untrained people can identify sick individuals above chance level by looking at facial photos taken 2 h after injection with a bacterial stimulus inducing an immune response (2.0 ng kg<sup>-1</sup> lipo ...[more]

PMID: 29298938

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Project description:Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3-33.1% for the skin model) to 89.4% (66.9-98.7%, for the nose model). Specificity ranged from 42.1% (20.3-66.5%) for the nose model and 94.7% (73.9-99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62-0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35-100.00%) and specificity of 42.11% (20.25-66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness.

Dataset Information

Identification of acutely sick people and facial cues of sickness.

Publications

Identification of acutely sick people and facial cues of sickness.

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