Background Most early lung cancers appear as pulmonary nodules on medical imaging, however, radiologists frequently overlook these on chest radiographs. We assessed if a deep learning-based artificial intelligence model can help detect pulmonary nodules on chest radiographs and compared its performance with board-certified human readers. Methods For this retrospective study, 308 chest radiographs were obtained between January 2019 to December 2021 from a tertiary care hospital. All radiographs were analyzed using a deep learning AI model called DxNodule AI Screen. Two expert board-certified radiologists established the ground truth, and 11 test readers independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results The standalone model had an AUROC of 0.905 [0.87, 0.94] in detecting pulmonary nodules. The mean AUROC across the 11 readers improved from 0.798 [0.74, 0.86] for unaided interpretation to 0.846 [0.82, 0.880] for AI-aided interpretation. With DxNodule AI Screen, readers were able to identify nodules at the correct locations, which they otherwise missed. The mean specificity, accuracy, PPV, and NPV of the readers improved significantly from 0.87 [0.78, 0.96], 0.78 [0.72, 0.84], 0.77 [0.65, 0.88], and 0.86 [0.81, 0.90] in the unaided session to 0.89 [0.82, 0.96], 0.83 [0.80, 0.85], 0.82 [0.73, 0.9], and 0.89 [0.86, 0.92], respectively in the aided session. Conclusion DxNodule AI Screen outperformed human readers in nodule detection performance on chest radiographs, and enhanced human readers’ performances when used as an aid.

Diffuse Alveolar Haemorrhage (DAH) is a severe respiratory complication of Systemic Lupus Erythematosus (SLE) and is associated with high mortality. A drop in blood haemoglobin, dyspnoea, haemoptysis, diffuse infiltrates on chest imaging indicate this devastating diagnosis. The DAH is rare in SLE, even rarer in the early months in an undiagnosed patient. Defective phagocytosis, immune complexes, depletion of complement, autoantibodies is the etiology. Immune complex induced alveolar capillaritis is the cause of DAH. This report was about a 28-year-old female, who presented with acute worsening dyspnoea on a background history of inflammatory joint pain, digital gangrene, alopecia, oral ulcers, and Raynaud’s phenomenon. She was subsequently diagnosed as SLE with DAH. This case was rare as she presented with DAH in the early months of her disease. Patient was started on high dose steroid, cyclophosphamide, and plasmapheresis, but succumbed on day 14 of admission due to high disease activity and respiratory failure. The DAH carries very high mortality rate even in best centres even when diagnosed early.

Pancytopenia is a reduction in all the three peripheral blood cell lineages and presents as anemia, leukopenia, and thrombocytopenia. Aplastic anemia is pancytopenia with bone marrow hypocellularity. Aplastic anemia can be constitutional or acquired. Genetic diseases such as Fanconi anemia and dyskeratosis congenita usually present as pancytopenia with typical physical anomalies and are usually seen in early childhood. Fanconi anemia is a rare cause of pancytopenia, which is an autosomal recessive disorder and manifests as progressive pancytopenia with congenital developmental anomalies and an increased risk of malignancy. Here, we present a case of Fanconi anemia who presented with pancytopenia, short stature, and hypoplastic thumb of the right hand and was diagnosed on the basis of bone marrow biopsy and chromosomal breakage test.

Background: Axial spondyloarthropathy is a type of disease which affects the axial skeleton affecting productive years. Methods: This was a cross-sectional, observational study in which 28 consecutive patients more than 16 years of age, fulfilling the Assessment of SpondyloArthritis International Society (ASAS) criteria for axial spondyloarthropathy were included. They were further sub-grouped into radiographic and non-radiographic axial spondyloarthropathy. Clinical features, joint involvement, measurements, HLA-B27 serology, and disease activity were evaluated. Data was entered into Microsoft Excel, and SPSS (Statistical Package for Social Sciences) software 2.0 was used for analyzing the data. Results: Mean age was 28.5 ± 6.3 years. 85.7% were males. Inflammatory low back pain was the most common clinical feature at presentation (89.2%). Enthesitis was the most common extra-articular feature seen in 35.7% of patients. 42.8% were non-radiographic axial spondyloarthritis. 85.7% of patients were HLA-B27 positive. 50% of patients had bone marrow edema on MRI, and only one patient had ankylosis indicating predominantly early disease. 50%–70% of our patients had high disease activity and 89.3% were responding well to non-steroidal anti-inflammatory drugs (NSAIDs). There was no significant difference between the radiographic axial spondyloarthritis group and the non-radiographic group except for elevated C-reactive protein (CRP). Conclusion: Ankylosing spondylitis in western India occurs mostly in the age group of 20–30 years, suggesting affection of productive age group. There was a delay of diagnosis for approximately three years from the onset of symptoms. There was a positive association with HLA-B27 in majority of the patients. Most of our patients had early disease based on radiological findings, suggesting that there was room for therapeutic intervention before irreversible ankylosis had set in.