Neonatal hypoxic-ischemic encephalopathy causes hypoxic brain injury. Due to differences in brain maturity at time of insult, severity of hypotension and duration of insult, there are four distinct patterns of brain injury. Magnetic resonance imaging is the most sensitive modality for evaluating these patterns of brain injury. Additional role of Diffusion weighted imaging and ADC values can be useful in the evaluation of such cases. We conducted this study to analyse the usefulness of ADC values in the brain tissue affected by hypoxic-ischemic injury. We conducted a prospective study of all the patients referred to our department for magnetic resonance scanning of brain with history of hypoxic ischemic encephalopathy and clinical features cerebral palsy. 23 Cases with imaging manifestations of hypoxic ischemic encephalopathy were included in the study. We studied distribution patterns of HIE in our cases and calculated the ADC values of involved as well as normal grey and white matter. Further, sensitivity, specificity, predictive values, and likelihood ratios for each dichotomized diffusion and ADC values were obtained Wilson Score method. The most common distribution pattern in our study was involvement of peri-rolandic area (15 cases, 65%). ADC values were significantly (p < 0.005) increased in abnormal white matter. No significant changes (p = 0.8) were seen in ADC values of normal and abnormal grey matter. Due to significant increase in ADC values of affected white matter, ADC value can be used as a marker to detect chronic sequel of hypoxic ischaemic brain injury. Another observation was the perirolandic brain tissue being most common area of involvement in the cases with cerebral palsy.

To determine the use of multi-detector computed tomography (MDCT) in the diagnostic interpretation of superior semicircular canal dehiscence (SSCD) or thinning and its association with ear pathologies and to find whether it is an acquired condition and its association with increase in age. study was performed in a tertiary care institute present in a village, following approval of the institutional ethical committee. Retrospective review of temporal bone CT examinations performed between September 2016 and March 2017 was done. 1 mm interval axial images with sagittal and coronal reformatted images were reviewed for the presence of canal dehiscence and thinning by investigators. We characterised the Superior semicircular canal status as normal, frank dehiscence or thinning. Frank dehiscence was further classified anatomically as anterior limb, apex and posterior limb dehiscence.The patient list was then subcategorized into 5 age groups, and the prevalence of SSCD was calculated for each group. Retrospective review yielded 80 positive cases which included SSC dehiscence (N = 39) and thinning (N = 41). 80 normal scans were selected as control group retrospectively. Statistical analysis was performed to assess for differences between the groups studied. Pearson chi-square test applied. there was a significant association of SSC pathologies prevalence with increasing age (p = < 0.001). No significant relationship was found between SSCD and presence of either CSOM or Cholesteatoma (p = 0.285). Vertigo rather than Tullio phenomenon was the statistically significant complaint (p = <0.001). which brought the patient to the hospital. The SSCD and thinning belong to the same spectrum and are acquired conditions. Increasing prevalence in old age suggests it to be an acquired condition rather than a congenital one. No significant association of these condition was seen with CSOM and cholesteatoma. Vertigo is the predominat symptom bringing the patient to hospital along with Tullio phenomenon.

Background and purpose World Health Organization estimated that there were 600,000 new cases of head and neck cancers and 300,000 deaths each year worldwide. Scientific modalities to predict the treatment outcomes are not available yet. We conducted this study to (1) compare CT perfusion parameters before and after chemoradiation among patients with head and neck squamous cell carcinoma and (2) to evaluate the prognostic value of each perfusion parameter in predicting the response to chemoradiation. Materials and methods We conducted a prospective study among all patients with head and neck squamous cell carcinoma registered for chemoradiotherapy (CRT) at Regional Cancer Research Center, Shimla, Himachal Pradesh, India during the period June 2012 through June 2013. CTp data were acquired on a 64-slice CT scanner (Light speed VCT Xte; GE Healthcare) with 14cm z-axis coverage using Volume Helical Shuttle (VHS) feature at baseline, on completion of 40Gy and 66Gy of chemoradiation. We dichotomised the treatment outcome as complete response and non-response (partial responders/stable disease/progressive disease) using RECIST 1.1 criteria. We compared all perfusion parameters at baseline, 40Gy and 66Gy of CRT between responders and non-responders. We dichotomised the perfusion parameters as high (>median value) and low (≤median value) to analyze association between perfusion parameters and treatment outcome. We calculated the sensitivity, specificity, predictive values, and likelihood ratios for each dichotomized perfusion parameter using Wilson Score method. Results We followed 24 patients (23 of them men) from start of the treatment till completion of it. All had Stage III or Stage IV of the disease. Blood flow (BF) and blood volume (BV) decreased and Mean Transit Time (MTT) increased significantly (p 106ml/100g/min) at baseline were five times more likely (p=0.004) to respond to treatment as compared to those with low BF. BF was found to be 83.3% predictive of complete response. Other perfusion parameters were not significantly predictive of outcome (p>0.05) Combination of high BF (>106ml/100g/min) and low (≤47ml/100g/min) permeability surface (PS) was 100% predictive of response to CRT irrespective of the stage of tumor. Conclusions High BF at baseline is the single best predictor of response to chemoradiaton. A combination of high BF and low PS was found to be 100% predictive of complete response irrespective of the stage of the tumor.

Artificial intelligence (AI), after surviving two major AI winters (1974-1980 and 1987-2000), is now growing at an exponential rate. This rapid advancement, particularly in its application to medical science and literature, has significantly transformed how research is conducted. The large language tools can produce highly realistic text, enabling diverse tasks with broad applications. In other words, their responses resemble human answers to human questions; however, their malicious use poses serious challenges to scientific research integrity and literature, especially when outputs influence human life, the ethical compass gains more importance than the benefits. This review aims to provide a comprehensive narrative review of AI, in particular the emergence of large language models and their impact on healthcare scientific research, with a focus on the challenges it poses to ethics and scientific integrity. In addition, it aims to discuss the evolving guidelines from various international organizations on authorship, transparency, and the responsible use of AI. Databases such as PubMed, Cochrane, Scopus, and Google Scholar were searched to provide a comprehensive review from the published literature on the emergence of AI in the healthcare research setting, along with its positive and negative impacts on research ethics. We also performed a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of AI in research publications and evaluated the ethical challenges it poses. Chatbots are AI-based conversational large language models, which are proving to be of significant importance in healthcare education, practice, and research. However, caution needs to be exercised in its malicious fabricated use. Organizations such as the Committee on Publication Ethics, World Association of Medical Editors, Journal of the American Medical Association, and International Committee of Medical Journal Editors state that chatbots do not qualify as co-authors, with only responsible and ethical use of AI being permitted. Caution needs to be exercised at the individual level by academics when they use these tools, and they should be transparent in their disclosure of their use. The advent of Google revolutionized scientific research, and similarly, AI-assisted chatbots represent the next leap forward. Hence, it is crucial to use these tools with caution, accountability, and transparency. Through this narrative review, we aim to guide researchers in understanding new guidelines and approaches to research ethics in this fast-evolving era of AI.

Radiology is one the most important and rapidly evolving modern diagnostic fields. It is facing significant challenges which makes it stand on crossroads in India. Regulatory pressures, technological shifts, and inter-specialty tensions pose real threats. While new technologies like Artificial Intelligence (AI) and teleradiology offer promising advances, they are accompanied by regulatory hurdles, professional encroachments, and workflow disruptions.