Shadan Institute of Medical Sciences

Whole Slide Imaging (WSI) has revolutionized modern pathology by enabling high-resolution digitization of tissue specimens, often exceeding 100,000 × 100,000 pixels, thereby supporting enhanced diagnostic interpretation and telepathology workflows. However, automated analysis of such gigapixel-scale data remains challenging due to computational complexity, staining variability, and morphological heterogeneity across samples. Deep learning-driven segmentation techniques particularly Fully Convolutional Networks (FCN), U-Net, and Mask R-CNN have demonstrated significant advances in tumor detection, cellular boundary delineation, and metastasis identification, with state-of-the-art studies reporting Dice similarity coefficients typically ranging from 0.85 to 0.92 in nuclei segmentation tasks. This review provides a focused and comprehensive overview of segmentation-oriented deep learning methodologies specifically designed for WSI in pathology. We summarize the current workflow including image digitization, patch extraction, data annotation, preprocessing strategies such as stain normalization and ROI enhancement, model selection, post-processing optimization, and clinical integration. By consolidating recent developments and outlining persistent gaps including limitations in labeled datasets, generalizability, hyperparameter sensitivity, and real-time deployment barriers this review offers practical insights to accelerate translation of deep learning segmentation into routine pathology practice. The work is intended as a valuable resource for clinicians, biomedical researchers, and developers engaged in computational pathology and precision diagnostics.

Background: Non-group 1 pulmonary hypertension, also known as secondary pulmonary hypertension (SPH), is predominantly observed among females. However, there is a significant lack of data concerning factors associated with hospitalization among patients diagnosed with SPH. This study aims to provide clinicians with vital insights for the identification of high-risk groups and for the more effective management of contributory risk factors within the female population affected by SPH. Methods: Using the 2019 National Inpatient Sample, we identified female admissions with SPH (n = 648,190), accounting for 3.8% of the total 17,236,228 female admissions. An Artificial Neural Network (ANN) analysis was conducted to evaluate predictive factors. We randomly allocated 3,319,543 patients into training and testing datasets at a ratio of 70:30, comprising 2,323,696 (70%) for training and 995,847 (30%) for testing, to calibrate and validate the performance of the ANN algorithm. Model performance was assessed by comparing misclassification rates between training and testing sets and by the area under the receiver operating characteristic curve (AUC); only internal validation was performed. Results: Females hospitalized with SPH were generally of older age, with a median of 75 years compared to 58 years, and more frequently identified as White (67.7% versus 65.5%) or Black (20.5% versus 15.5%) relative to those without SPH. They also demonstrated a higher prevalence of most atherosclerotic cardiovascular disease (ASCVD) risk factors or their equivalents, including complicated hypertension (50.6% versus 17.8%), diabetes with chronic complications (30.6% versus 13.7%), and hyperlipidemia (50.8% versus 29.2%), as well as other comorbidities such as COPD (43.4% versus 20.2%) and CKD (43.3% versus 14.0%), and exhibited increased all-cause mortality (4.5% versus 1.8%) (p < 0.001). Our ANN model achieved an AUC of 0.823, indicating good predictive capability. The rates of incorrect predictions were comparable in both the testing and training cohorts, at 3.8% each. The factors most strongly associated with a coded SPH diagnosis included age at admission, complicated hypertension, chronic kidney disease, chronic obstructive pulmonary disease, uncomplicated hypertension, prior VTE, race, arthropathies, and AIDS. Conclusions: Our ANN model identified demographic and comorbidity factors associated with a coded SPH diagnosis among hospitalized females, with good discrimination (AUC = 0.823). Because the model classifies the presence of an existing diagnosis rather than predicting future hospitalization, and was validated only internally, external and prospective validation is required before clinical application. Once validated, these factors could support individualized, sex-specific risk stratification for high-risk female populations, consistent with the goals of personalized medicine.

Acquired small intestinal diverticulitis and its complications remain poorly understood due to their rarity and nonspecific presentation. Complications such as abdominal sepsis, bleeding, and perforation can lead to unfavorable outcomes. We report the case of a patient who presented with acute jejunal diverticulitis requiring surgical intervention. A 66-year-old man with a history of endovascular abdominal aortic aneurysm repair presented with acute left lower quadrant abdominal pain and tenderness. Computed tomography (CT) detected intestinal diverticulosis, localized free air, and inflammatory changes associated with the small intestine. Exploratory laparoscopy revealed numerous proximal jejunal diverticula with perforation contained within the mesentery. A 48 cm segment of the jejunum was resected, and a primary anastomosis was performed. Surgical pathology confirmed acute small intestinal diverticulitis and serositis. The patient had an uneventful postoperative course and was discharged home on postoperative day 6. This case highlights the diagnostic challenge in acute small intestinal diverticulitis, which may present with nonspecific signs and inconclusive radiological findings. The location of diverticula along the mesenteric border may contain perforations in the mesentery and obscure peritoneal signs. Although some patients with acute small intestinal diverticulitis may be conservatively managed, surgical intervention remains the standard of care for managing patients with complications. Acute small intestinal diverticulitis, though exceedingly rare, can carry a significant risk of morbidity and mortality. Thus, small intestinal diverticulitis should be considered in the differential diagnosis in older adults who present with acute abdominal pain and tenderness. Surgical management remains the gold standard of treatment of severe acute small intestinal diverticulitis.

Introduction Assessment format may influence the extent to which student learning approaches are reflected in performance, yet whether constructed-response descriptive assessments (DAs) and selected-response multiple-choice question assessments (MCQAs) differ in their sensitivity to variation in learning approach within formative physiology education has not been directly examined. This study tested whether baseline deep and surface learning approaches were differentially associated with performance in DAs and MCQAs within a longitudinal formative undergraduate medical physiology programme. Methods This three-month longitudinal observational study was conducted at a single medical college in South India. Among 150 invited first-year medical students, 109 completed the baseline Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) and contributed to the study. Eight physiology topics, selected through a modified Delphi process, were taught sequentially and assessed on a rolling basis. For each topic, students completed both a DA and an MCQA in the same sitting, with items matched on construct and revised Bloom's taxonomy level. A linear mixed-effects model was used to test whether the association between learning approach and marks differed by assessment format, with a student-level random intercept to account for repeated observations. A post hoc inter-rater reliability audit was conducted on a randomly selected subset of DA scripts. Sensitivity analyses included a random-slopes model and a Deep-minus-Surface composite parameterisation. Results The association between learning approach and marks differed by assessment format, with significant format-by-deep-learning and format-by-surface-learning interactions (both p < 0.001). Within DAs, higher deep-learning scores were associated with higher marks (β = 0.032, p = 0.022), whereas higher surface-learning scores were associated with lower marks (β = -0.038, p = 0.003). Within MCQAs, neither learning-approach dimension was significantly associated with marks. The negative association between surface learning and DA performance remained robust across model specifications, whereas the positive association between deep learning and DA performance was attenuated in the random-slopes model (p = 0.072). The Deep-minus-Surface sensitivity analysis supported the same overall format-dependent pattern. A post hoc inter-rater reliability audit on 25% of DA scripts yielded strong agreement with an intraclass correlation coefficient (ICC) of 0.87. Conclusions Assessment format moderated the association between learning approach and assessment performance in this cohort: DAs showed clearer differentiation by learning approach than MCQAs, with poorer DA performance at higher surface-learning scores. These findings do not show that MCQAs reward surface learning but suggest that DAs may provide more discriminating information about variation in learning approach than MCQAs within a programmatic assessment framework.