Publications by authors named "Abdukhamid Radzhabov"
: Acute Cystitis Symptom Score (ACSS) is a self-reporting questionnaire for clinical diagnosis and follow-up of acute uncomplicated cystitis (AC) in women. The ACSS, originally developed in Uzbek and Russian, both considered original languages, is now available in several other languages. This study aimed to translate and validate the ACSS in the Tajik language.
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- A retrospective study evaluated a deep learning AI tool for detecting prostate cancer and grading its severity in biopsy samples, aiming to streamline the time-consuming process of analyzing prostate biopsies.
- The study involved analyzing 5 external patient cohorts, using a total of 5922 tissue sections, and demonstrated the AI's ability to accurately detect tumor presence that some pathologists missed, achieving high sensitivity and specificity scores.
- The AI tool showed comparable accuracy to experienced pathologists in Gleason grading, indicating its potential as a reliable assistant in prostate cancer diagnosis and assessment.
Purpose: To reassess the diagnostic values of the "draft" guidelines for the clinical diagnosis of acute uncomplicated cystitis (AC), recently proposed by US Food and Drug Administration (FDA) and European Medicines Agency (EMA).
Methods: The data of 517 female respondents (patients with acute cystitis and controls) derived from the e-USQOLAT database were analyzed and used for the validation of proposed "draft" guidelines of FDA and EMA, compared to the Acute Cystitis Symptom Score (ACSS) questionnaire. The diagnostic values of the proposals concerning signs, symptoms and their severity were assessed and compared.