Chandigarh: A joint study by Punjabi University, Patiala, and PGIMER, Chandigarh, has developed artificial intelligence-based methods to aid diagnosis of Autoimmune Blistering Diseases (AIBDs). The rare skin disorders are marked by severe blistering and are considered clinically complex and difficult to diagnose.The university officials said that the study received formal ethical clearance from the Institutional Ethics Committees of both institutions and approval from the Collaborative Research Committee of PGIMER. The research was carried out by PhD scholar Manbir Singh under the supervision of Maninder Singh of the department of computer science, Punjabi University, Patiala, and the co-supervision of Prof Dipankar De of the department of dermatology, PGIMER, Chandigarh. Manbir Singh said diagnosing AIBDs was exceptionally difficult because accurate confirmation required multiple specialised investigations. Many tests were highly expensive and time-consuming, leading to significant delays in confirming the disease. The diagnostic process became more complicated because AIBDs comprised several subtypes that shared heavily overlapping clinical features.Maninder Singh said Direct Immunofluorescence was the gold standard test for confirming AIBDs, complemented by Indirect Immunofluorescence and Enzyme-Linked Immunosorbent Assay tests, but these investigations were constrained by high costs, long turnaround times and the need for high-level technical expertise, and were typically available only in specialised tertiary care institutions. He said the new AI tool would be immensely helpful for clinicians in primary care settings and rural healthcare centres, where access to specialised laboratory tests and expert dermatology consultation was highly limited.The research team said extensive AI research focused on classifying common skin diseases, but AI research on rare and complex conditions such as AIBDs remained severely limited, primarily because of the absence of a clinically validated, publicly available dataset. To address this, the team created a clinically validated dataset by collecting authentic clinical images from patients diagnosed and treated at the dermatology department of PGIMER, Chandigarh.Prof Dipankar De said the images were meticulously annotated under expert supervision only after complete clinical and diagnostic confirmation of the disease. He said the study evaluated the performance of classical machine learning, hybrid models and advanced deep learning approaches and carried out a comprehensive evaluation of nearly 240 model configurations. He told that to assess practical utility, the diagnostic performance of the developed AI models was compared directly against dermatologists with varying levels of professional experience, and the developed models consistently outperformed the participating dermatologists in accurately classifying distinct AIBD subtypes.The research team said it aimed to expand the dataset by incorporating more clinical images of rare AIBD subclasses and add comprehensive patient metadata, including age, sex, demographic backgrounds, laboratory test reports and specific lesion locations. It said future iterations would integrate Vision Transformer-based models and longitudinal disease monitoring systems to boost classification performance and real-world clinical.The vice-chancellor of Punjabi University, Patiala, said such innovative research stood as a prime example of how modern technology could revolutionise human healthcare and medical science, and said the joint venture between Punjabi University and PGIMER would prove to be a true boon for the general public and underserved primary health sectors in the times to come. MSID:: 131567414 413 |
