Investigating face perception in humans and DCNNs

This thesis aims to compare strengths and weaknesses of AI and humans performing face identification tasks, and to use recent advances in machine-learning to develop new techniques for understanding face identity processing. By better understanding underlying processing differences between Deep Convolutional Neural Networks (DCNNs) and humans, it can help improve the ways in which AI technology is used to support human decision-making and deepen understanding of face identity processing in humans and DCNNs. In Chapter 2, I test how the accuracy of humans and DCNNs is affected by image quality and find that humans and DCNNs are affected differently. This has important applied implications, for example, when identifying faces from poor-quality imagery in police investigations, and also points to different processing strategies used by humans and DCNNs. Given these diverging processing strategies, in Chapter 3, I investigate the potential for human and DCNN decisions to be combined in face identification decisions. I find a large overall benefit of 'fusing' algorithm and human face identity judgments, and that this depends on the idiosyncratic accuracy and response patterns of the particular DCNNs and humans in question. This points to new optimal ways that individual humans and DCNNs can be aggregated to improve the accuracy of face identity decisions in applied settings. Building on my background in computer vision, in Chapters 4 and 5, I then aim to better understand face information sampling by humans using a novel combination of eye-tracking and machine-learning approaches. In chapter 4, I develop exploratory methods for studying individual differences in face information sampling strategies. This reveals differences in the way that 'super-recognisers' sample face information compared to typical viewers. I then use DCNNs to assess the computational value of the face information sampled by these two groups of human observers, finding that sampling by 'super-recognisers' contains more computationally valuable face identity information. In Chapter 5, I develop a novel approach to measuring fixations to people in unconstrained natural settings by combining wearable eye-tracking technology with face and body detection algorithms. Together, these new approaches provide novel insight into individual differences in face information sampling, both when looking at faces in lab-based tasks performed on computer monitors and when looking at faces 'in the wild'

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)