Diabetic foot ulcers are common, recurrent, leading frequently to foot amputation and even death. Their management requires early expert infection assessment and remains a major challenge for the clinicians. Assessment also necessitates culture-sensitivity of the swab taken from ulcer (the gold-standard technique) to identify the bacteria colonizing the infected wound. The process requires accurate swabbing, culturing in a BSL-2 facility and takes anywhere between 2-5 days leading to prescription of generic antibiotics by the doctors. Regular swabbing is a cumbersome procedure to understand and regularly follow up on the microflora population.
Each bacteria has characteristic emission fluorescence when excited with different wavelength of light sources. A novel device, developed by us, leverages this auto-fluorescence property enabling us to develop a multispectral imaging platform. The device captures the spectral signatures of metabolic growth markers along with markers released when a microbiome causes infection to detect and assess the bacterial gram type.
A preliminary clinical study was conducted at MV Hospital for Diabetes and Prof M Viswanathan Diabetes Research Centre, Chennai. Of the 50 patients imaged, the spectral signatures obtained from our device was able to find significant differences between gram positive and gram-negative bacteria. The device spectral results was compared against deep tissue culture biopsy and the device was able to detect gram positives and gram negatives with 83% and 81% accuracy respectively. The device also picked up 7 polymicrobial sites.
In summary, the device can be used as an important tool in guided swabbing, assessment of a wound and understanding its microbiome pattern. The device helps to differentiate infected from non infected wounds, classifies the infected ones broadly according to their gram type and enables real time follow up of wounds. In future, fluorescence spectral signatures will be obtained using more excitation wavelengths to differentiate the exact species of bacteria and to improve on the accuracy of classification to enable treatment protocols using tailored antibiotics.