## 3 Discussion
This lack of agreement in the visual assessment of a wound, whether by verbally reporting what is seen at the time of inspection or by use of digital photography [18, 19], is concerning and resonates with current concerns across the UK National Health Service (NHS) of the unwarranted variations in medical practice, now a national programme for improvement (Getting it right first time (GIRFT) [20]. With reference to wound care, standards and treatments remain plagued by myths and inconsistency [21], resulting in escalating treatment costs, particularly those complex chronic wounds where identification and assessment of slow or stalled healing continue to be a management challenge. It is a testament to the issues driving the need for improvement that wound care recently became a topic of debate in the House of Lords. To supersede the conventional 'wisdom' of what clinicians recognise as a problem and then to translate this learning to specific wound features via AI automation requires a detail of the many layers of judgement that is made almost unconsciously many times a day. This backdrop to wound care nationally, and the recognition that new diagnostic technologies are needed in the setting of wound care [22] is a recommendation aligning well to the need to make progress in wound assessment. It is now clear from a number of reports that rapid developments in computer processing will revolutionise automation of wound tracking and assessment to improve the ability to predict wound healing outcomes. However, although automated documentation of wound metrics such as depth, size and volume indicate wound healing progress [23] with devices available commercially ([[https://www.researchmarkets.com/reports/55](https://www.researchmarkets.com/reports/55)]([https://www.researchmarkets.com/reports/55](https://www.researchmarkets.com/reports/55)) 78241/digital-wound-measurement-devices-market-by#product-summary, accessed on 15 March 2020), AI-based systems for pattern recognition and feature extraction for wound healing prognosis are still in their infancy [24]. Automation and wound image recognition starts with the region of interest; including aspects that should be included and those that should be omitted in the analysis. Focus is then on 'object' definitions that warrant classifying as predictors of complications. Turning to the wound as an image obtained during the course of routine clinical care, there remains a need for the clinician to interpret the image and to identify 'objects' that can then be classified to a specific pathological category, understood by the clinician and relevant to the patients presenting condition. This is the case irrespective of how the digital wound picture or scan was derived. For example, in the event of imaging using computed tomography  
Figure 10: Cold spots in the wound site at day 2 of a woman who proceeded to develop SSI after caesarean section (**A**) and a woman with an uncomplicated wound recovery period (**B**). (CT) or Colour Doppler [25]). In this study, we have focused on infrared thermography of acute incisional wounds where automation has potential for predicting healing outcomes providing that the segmentation process is reliable. As a starting point, image data were already available as infrared maps of the abdomen and wound site through laborious image post processing undertaken by a single investigator with many years of experience of both wound assessment and thermal imaging. By introducing two novice reviewers to review the images, the problem inherent in technology development has been highlighted; accurate segmentation and image boundaries need to be established at the earliest stages of image automation. These are based on the way people recognize objects and features that ultimately provide a route to reliable, independent wound healing risk stratification and ultimately diagnostic or prognostic signatures of later complications.