Purpose Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30–40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces. Methods We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images. Results Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative. Conclusion Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of “unaffected” relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.

Appearance and facial function are concepts not well addressed in current pediatric patient-reported outcome measures (PROM) for facial conditions. We aimed to develop a new module of the FACE-Q for children/young adults with facial conditions that include ear anomalies, facial paralysis, skeletal conditions, and soft tissue conditions. Semi-structured and cognitive interviews were conducted with patients aged 8–29 years recruited from craniofacial centers in Canada, USA, UK, and Australia. Interviews were used to elicit new concepts and to obtain feedback on CLEFT-Q scales hypothesized to be relevant to other facial conditions. Interview data were recorded, transcribed, and coded. Experts were emailed and invited to provide feedback via Research Electronic Data Capture (REDCap). Eighty-four participants and 43 experts contributed. Analysis led to the development of a conceptual framework and 14 new scales that measure appearance, facial function, health-related quality of life, and adverse effects of treatment. In addition, 12 CLEFT-Q scales were determined to have content validity for use with other facial conditions. Expert input led to minor changes to scales and items. This new FACE-Q module for children/young adults is being field-tested internationally. Once finalized, we anticipate this PROM will be used to inform clinical practice and research studies.

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Infants with craniosynostosis involving the metopic and coronal sutures require cranio-orbital reshaping to correct craniofacial dysmorphologic feature and to improve facial balance. Currently, surgical techniques to create a balanced fronto-orbital region are based on the surgeon's subjective approach and artistic vision in creating a normal shape to the forehead. To date, the use of age-matched templates and computer-assisted design/computer-assisted manufacturing techniques in optimizing the outcomes of surgical intervention in this area have not been explored. The aim of this article was to describe the process of template generation and application based on age-matched controls using computer-assisted design/computer-assisted manufacturing technology and to present this application in 2 cases.

Background This study aimed to determine the impact of pulmonary complications on death after surgery both before and during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Methods This was a patient-level, comparative analysis of two, international prospective cohort studies: one before the pandemic (January–October 2019) and the second during the SARS-CoV-2 pandemic (local emergence of COVID-19 up to 19 April 2020). Both included patients undergoing elective resection of an intra-abdominal cancer with curative intent across five surgical oncology disciplines. Patient selection and rates of 30-day postoperative pulmonary complications were compared. The primary outcome was 30-day postoperative mortality. Mediation analysis using a natural-effects model was used to estimate the proportion of deaths during the pandemic attributable to SARS-CoV-2 infection. Results This study included 7402 patients from 50 countries; 3031 (40.9 per cent) underwent surgery before and 4371 (59.1 per cent) during the pandemic. Overall, 4.3 per cent (187 of 4371) developed postoperative SARS-CoV-2 in the pandemic cohort. The pulmonary complication rate was similar (7.1 per cent (216 of 3031) versus 6.3 per cent (274 of 4371); P = 0.158) but the mortality rate was significantly higher (0.7 per cent (20 of 3031) versus 2.0 per cent (87 of 4371); P < 0.001) among patients who had surgery during the pandemic. The adjusted odds of death were higher during than before the pandemic (odds ratio (OR) 2.72, 95 per cent c.i. 1.58 to 4.67; P < 0.001). In mediation analysis, 54.8 per cent of excess postoperative deaths during the pandemic were estimated to be attributable to SARS-CoV-2 (OR 1.73, 1.40 to 2.13; P < 0.001). Conclusion Although providers may have selected patients with a lower risk profile for surgery during the pandemic, this did not mitigate the likelihood of death through SARS-CoV-2 infection. Care providers must act urgently to protect surgical patients from SARS-CoV-2 infection.