How do Australian podiatrists manage patients with diabetes The Australian diabetic foot management survey

Background Diabetic foot complications are the leading cause of lower extremity amputation and diabetes-related hospitalisation in Australia. Studies demonstrate significant reductions in amputations and hospitalisation when health professionals implement best practice management. Whilst other nations have surveyed health professionals on specific diabetic foot management, to the best of the authors’ knowledge this appears not to have occurred in Australia. The primary aim of this study was to examine Australian podiatrists’ diabetic foot management compared with best practice recommendations by the Australian National Health Medical Research Council. Methods A 36-item Australian Diabetic Foot Management survey, employing seven-point Likert scales (0 = Never; 7 = Always) to measure multiple aspects of best practice diabetic foot management was developed. The survey was briefly tested for face and content validity. The survey was electronically distributed to Australian podiatrists via professional associations. Demographics including sex, years treating patients with diabetes, employment-sector and patient numbers were also collected. Chi-squared and Mann Whitney U tests were used to test differences between sub-groups. Results Three hundred and eleven podiatrists responded; 222 (71%) were female, 158 (51%) from the public sector and 11–15 years median experience. Participants reported treating a median of 21–30 diabetes patients each week, including 1–5 with foot ulcers. Overall, participants registered median scores of at least “very often” (>6) in their use of most items covering best practice diabetic foot management. Notable exceptions were: “never” (1 (1 – 3)) using total contact casting, “sometimes” (4 (2 – 5)) performing an ankle brachial index, “sometimes” (4 (1 – 6)) using University of Texas Wound Classification System, and “sometimes” (4 (3 – 6) referring to specialist multi-disciplinary foot teams. Public sector podiatrists reported higher use or access on all those items compared to private sector podiatrists (p < 0.01). Conclusions This study provides the first baseline information on Australian podiatrists’ adherence to best practice diabetic foot guidelines. It appears podiatrists manage large caseloads of people with diabetes and are generally implementing best practice guidelines recommendations with some notable exceptions. Further studies are required to identify barriers to implementing these recommendations to ensure all Australians with diabetes have access to best practice care to prevent amputations.

Deploying speaker verification techniques to vision-based RPA detect and avoid

Detect and Avoid (DAA) technology is widely acknowledged as a critical enabler for unsegregated Remote Piloted Aircraft (RPA) operations, particularly Beyond Visual Line of Sight (BVLOS). Image-based DAA, in the visible spectrum, is a promising technological option for addressing the challenges DAA presents. Two impediments to progress for this approach are the scarcity of available video footage to train and test algorithms, in conjunction with testing regimes and specifications which facilitate repeatable, statistically valid, performance assessment. This paper includes three key contributions undertaken to address these impediments. In the first instance, we detail our progress towards the creation of a large hybrid collision and near-collision encounter database. Second, we explore the suitability of techniques employed by the biometric research community (Speaker Verification and Language Identification), for DAA performance optimisation and assessment. These techniques include Detection Error Trade-off (DET) curves, Equal Error Rates (EER), and the Detection Cost Function (DCF). Finally, the hybrid database and the speech-based techniques are combined and employed in the assessment of a contemporary, image based DAA system. This system includes stabilisation, morphological filtering and a Hidden Markov Model (HMM) temporal filter.