Septic arthritis, osteomyelitis, and cellulitis in a neonate: A case report and discussion of diagnosis and management.

INTRODUCTION: Septic arthritis, osteomyelitis, and cellulitis are rare but life-threatening conditions in neonates. This case report emphasizes the significance of early diagnosis and intervention in such cases. PRESENTATION OF CASE: We present a case of a male neonate, who developed septic arthritis, osteomyelitis, and cellulitis following trauma from a fall. The patient presented with fever, swelling, and limited joint movement. Diagnosis was based on clinical evaluation, laboratory investigations, and imaging studies. Treatment involved intravenous antibiotics for three weeks, followed by oral antibiotics for two weeks, resulting in complete recovery. DISCUSSION: Neonatal septic arthritis and osteomyelitis are challenging to diagnose due to nonspecific symptoms. Early initiation of antibiotics is crucial to prevent long-term complications. Surgical intervention may be required in cases of inadequate antibiotic response or significant joint effusion. This case underscores the importance of prompt recognition and tailored management. CONCLUSION: Septic arthritis, osteomyelitis, and cellulitis pose serious threats to neonates. Timely diagnosis, appropriate antibiotics, and, if needed, surgical intervention are vital for favorable outcomes. Individualized treatment plans should consider clinical condition and local protocols.

Machine vision-based Statistical texture analysis techniques for characterization of liver tissues using CT images.

OBJECTIVE: To characterize human liver tissues by demonstrating the ability of machine vision, and to propose a new auto-generated report based on texture analysis that may work with co-occurrence matrix statistics. METHODS: The retrospective study was conducted at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, and comprised clinically verified computed tomography imaging data between October 2018 and September 2020. The image samples and related data were used to segregate classes 1-4. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues using supervised learning methods, principal component analysis, linear discriminant analysis, and non-linear discriminant analysis. Robust and reliable texture features were investigated by generating testing classes. Overall performance of the presented machine vision approach was analyzed using four parameters; precision, recall/sensitivity, F1-score, and accuracy. Statistical analysis was done using B11 software. RESULTS: There were 312 image samples from 71 patients; 51(71.8%) males and 20(28.2%) females. Among the patients, 19(26.7%) had abscess, 15(21.1%) had metastatic disease, 23(32.4%) had tumour necrosis, 6(8.5%) had vascular disorder, and 8(11.3%) were normal. Principal component analysis, linear discriminant analysis, and non-linear discriminant analysis showed high >97.86% values, but the discrimination rate was 100% for class 4. CONCLUSIONS: Abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques using second-order statistics that may assist the radiologist and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.