Ms. Geethanjali Radhakrishnan Profile

Geethanjali Radhakrishnan

CEO & MD at Adiuvo Diagnostics Ltd

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Publications (8)

Proceedings Article | 13 March 2024 Presentation

An accurate and rapid method (<10 min) to detect biofilms in wounds using a combination of wound blotting, white light imaging, and machine learning

Jagdish A. Krishnaswamy, Keerthana S., Krishna Sree S., Janakrai N. Parekh, Geethanjali Radhakrishnan, Bala Pesala

Proceedings Volume PC12832, PC128320E (2024) https://doi.org/10.1117/12.3003622

KEYWORDS: Biofilms, Machine learning, Wound healing, Bacteria, Resistance, Moisture, Medicine, Matrices, Mathematical optimization, Gold

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Proceedings Article | 13 March 2024 Presentation

Deep-learning aided multispectral autofluorescence imaging for rapid bioburden detection and gram-type classification on diabetic foot ulcers

Aravind Sridhar, Ragavendiran Balasubramanian, Jagdish A. Krishnaswamy, Geethanjali Radhakrishnan, Bala Pesala

Proceedings Volume PC12832, PC1283208 (2024) https://doi.org/10.1117/12.3003591

KEYWORDS: Diabetic foot ulcers, Bacteria, Auto-fluorescence imaging, Multispectral imaging, Autofluorescence, Deep learning, Ultraviolet radiation, Resistance, Point-of-care devices, Education and training

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Proceedings Article | 13 March 2024 Presentation

Multispectral terahertz and autofluorescence imaging for tumor classification in breast and oral fresh tissue

Jyotirmayee Dash, Geethanjali Radhakrishnan, Arun Jana, Lenin B., Shyamsunder Mandayam, Bala Pesala

Proceedings Volume PC12834, PC128340D (2024) https://doi.org/10.1117/12.3003955

KEYWORDS: Cancer, Tissues, Breast, Auto-fluorescence imaging, Tumors, Terahertz technology, Evolutionary algorithms, Cancer detection, Surgery, Machine learning

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Proceedings Article | 12 March 2024 Presentation + Paper

Illuminate-τ: a portable platform combining autofluorescence lifetime characteristics and machine learning for rapid detection of <100 CFU/ml bioburden in water

Jagdish Krishnaswamy, Krishna Sree S., Swati Krishna, Mohamed Irfan, Geethanjali Radhakrishnan, Bala Pesala

Proceedings Volume 12854, 1285402 (2024) https://doi.org/10.1117/12.3003634

KEYWORDS: Autofluorescence, Biological samples, Pathogens, Detection and tracking algorithms, Scattering, Portability, Manufacturing, Industry, Machine learning, Bacteria

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Bioburden detection in water is an important challenge in the contexts of both consumer and industrial water. Water-borne infections due to bacteria and fungi are becoming key public health concerns. Further, in biologics manufacturing sector, it is of key importance to use water with zero bioburden in all critical manufacturing processes. However, current methods of detecting and classifying bioburden in water samples is a tedious process involving time-consuming microbiological steps where it takes about 5-7 days to infer trace levels of pathogen contamination. It is possible to hasten the detection process using cytometry-based platforms that offer high levels of sensitivity and specificity in pathogen detection and enumeration of various pathogens. However, these solutions may not be able to determine the bacterial species and viability in a label-free set up. Here, we present Illuminate-τ, a portable label-free detection platform for rapid bioburden assessment in water samples in under 10 minutes, thereby demonstrating a >1000x improvement in detection time. This works on the principle of inferring the presence of pathogen cells in water through their native autofluorescence lifetime characteristics. The Illuminate-τ device is integrated with pulsed nanosecond UV light sources and drivers, high-speed single photon avalanche detectors, and sophisticated timing circuits controlled by an embedded electronics subsystem. In addition, the device also has an edge inferencing SVM-based machine learning algorithm that takes in autofluorescence lifetime characteristics and detects <100 CFU/ml of bioburden such as Pseudomonas, E.coli, Salmonella, Candida species etc. with an accuracy exceeding 99%. Further, we show that we the platform is also able to differentiate Pseudomonas from other bacteria and fungi with a 100% accuracy under similar conditions of concentration. In summary, we demonstrate that Illuminate-τ a device based on autofluorescence lifetime coupled with machine-learning-based detection strategies can achieve high bioburden detection sensitivity.

Proceedings Article | 16 March 2023 Paper

Portable device for rapid assessment of pathogens using autofluorescence lifetime characteristics

Jagdish Krishnaswamy, Harish Kumar D., Mohamed Irfan, Krishna Sree S., Bala Pesala, Geethanjali Radhakrishnan

Proceedings Volume 12391, 1239102 (2023) https://doi.org/10.1117/12.2653181

KEYWORDS: Autofluorescence, Portability, Tissues, Pathogens, Cancer detection, Biological samples, Skin, Point-of-care devices, Fluorophores

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Proceedings Article | 6 March 2023 Presentation

Point-of-care device for rapid phenotypic antibiotic susceptibility testing using pathogen autofluorescence

Keerthana S., Krishna Sree S., Harish Kumar D., Mohamed Irfan, Jagdish A. Krishnaswamy, Bala Pesala, Geethanjali Radhakrishnan

Proceedings Volume PC12369, PC1236908 (2023) https://doi.org/10.1117/12.2653178

KEYWORDS: Point-of-care devices, Pathogens, Resistance, Bacteria, Tissues, Skin, Optical filters, Medicine, CMOS cameras

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Proceedings Article | 19 February 2020 Paper

Rapid handheld screening device to detect skin and soft tissue infections

Geethanjali Radhakrishnan, Aayush Gupta, John King, Devina Ganvir

Proceedings Volume 11211, 112110V (2020) https://doi.org/10.1117/12.2546404

KEYWORDS: Bacteria, Skin, Pathogens, Tissues, Multispectral imaging, Imaging devices, Image processing, Machine learning

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Proceedings Article | 4 March 2019 Presentation

Rapid reagent-less bacterial screening with identification of gram type in diabetic foot ulcers (Conference Presentation)

Geethanjali Radhakrishnan, Vijay Vishwanathan, John King, Raghul Kumar, Meena Udayasankar

Proceedings Volume 10869, 108690T (2019) https://doi.org/10.1117/12.2509518

KEYWORDS: Diabetic foot ulcers, Bacteria, Luminescence, Light sources, Multispectral imaging, Tissues, Biopsy

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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.

Showing 5 of 8 publications

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