Dr Saicharan Kulkarni MD

  Abstract Secondary adrenal insufficiency (SAI) is a rare but potentially life-threatening complication of autoimmune connective tissue disorders such as mixed connective tissue disease (MCTD). We present a case of a middle-aged woman with MCTD who developed SAI. She exhibited cutaneous hyperpigmentation, particularly over the face, palmar creases, and soles, but lacked oral mucosal pigmentation. These findings, supported by biochemical tests and imaging, were consistent with a diagnosis of SAI rather than primary adrenal insufficiency (PAI). This case emphasizes the value of cutaneous findings in early recognition and differentiation of adrenal insufficiency subtypes. Keywords Secondary adrenal insufficiency, mixed connective tissue disease, hyperpigmentation, oral mucosa, ACTH, autoimmune Introduction Adrenal insufficiency may be classified as primary (PAI), secondary (SAI), or tertiary, depending on the level of the hypothalamic-pituitary-adrenal (HPA) axis affected. SAI r...

Project Title:  Feasibility of Automated Bone Age Estimation viaGoogle Teachable Machine: A Proof-of-Concept Study   CHAPTER 1: INTRODUCTION 1.1 Background of the Study Bone age assessment (BAA) is an essential diagnostic tool in pediatric endocrinology. Manual methods, such as the Greulich-Pyle (GP) atlas and Tanner-Whitehouse (TW2/TW3) scoring, require radiologists to visually compare hand radiographs to reference standards. These methods are effective, but time-intensive, subjective, and susceptible to inter- and intra-observer variability. Advances in artificial intelligence (AI), particularly in deep learning, offer promising alternatives. Convolutional Neural Networks (CNNs) have demonstrated strong performance, with mean absolute errors as low as 6–8 months in benchmark studies such as the RSNA Pediatric Bone Age Challenge.   1.2 Relevance of the Project in AI   This study evaluated the use of no-code AI platforms, specifically Google Teachable Machine (GTM), ...