Publications by authors named "H N Alshaikh"

Objectives: To report our institution's experience with patients who underwent subtotal petrosectomy (STP) for refractory ear disease who are candidates for cochlear implant (CI) and to highlight simultaneous STP with CI its advantages, surgical outcomes, post-operative complications management, and considerations for staging a procedure.

Methods: A retrospective study was performed in a single tertiary referral university hospital. The medical records of seventy patients (70 ears) who underwent STP for refractory ear disease who were candidates for CI were retrospectively evaluated.

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The coronavirus disease 2019 (COVID-19) pandemic has affected healthcare systems worldwide, with mandatory quarantine and isolation measures being implemented to curb the spread of the virus. These measures have potentially led to delayed or complicated presentations of non-COVID-19 cases, including pediatric surgical cases. This study aims to evaluate pediatric surgical admission patterns, analyze the incidence of surgical diagnoses, and assess the severity of presentation during the COVID-19 period compared to the pre-COVID-19 period.

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Purpose: Otolaryngology has experienced notable advancements and growth in the application of artificial intelligence (AI). However, otolaryngologists' perception of these tools are lacking. This study aims to assess the knowledge and attitudes of otolaryngologists toward AI.

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Objective: Clinicians have noted that the ongoing war on Gaza has had a psychological impact on people with multiple sclerosis (PwMS) whose mental health is particularly vulnerable during stressful times such as these. The present study sought to examine the prevalence and correlates of posttraumatic stress disorder (PTSD) symptoms among PwMS in Jordan during the Gaza war outbreak.

Methods: This was a cross-sectional study involving 171 patients (PwMS) out of 372 patients approached (46% response rate) who were being seen in the MS unit at Al Bashir Hospital, Ammann, Jordan.

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Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result.

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