Objectives: To investigate response of the community pharmacists to acute back pain consultations by the patients, using doctor of pharmacy degree (Pharm D) final year students as simulated patients. Method:The study was a cross-sectional study, conducted over a three months period from April 2016 to July 2016 in community pharmacy setting. The study was done at the western area in Saudi Arabia, at three cities: (Jeddah-Mecca-Taif). Convenient sample of 300 pharmacies were chosen. The students appeared in the pharmacy as patients. A check list was filled immediately after the visit. Results: Results of the study showed that most of pharmacies studied were chain pharmacies (67.3%). The response of the pharmacists towards patients' counseling ranges from (6-55%). The majority of the pharmacists did not ask about spreading of the pain (93%), other disease (91%), medications taken (90%), intensity of pain (83.67%), and duration (83%). There was a low response of pharmacists towards self-care, except rarely, who gave advice for back exercise (6.667%). About 98% and above, did not give information about expected drug problems. Referral was given mainly for numbness symptoms (10.67%). Types of medications recommended were diclofenac sodium (44.67%), muscle relaxants (28.67%) and topical NSAIDS (25.33%). About 70%, of the pharmacists did not give advice spontaneously. Counseling time was less than one minute in 67.67% of the cases. Conclusions: The response of community pharmacists, to acute back pain consultations was inadequate, which necessitates educational and training programs in minor disease treatment, and availability of national guidelines.

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In this work, we present a novel Artificial Intelligence (AI) powered non-destructive testing (NDT) system for the detection of potential corrosion under insulation (CUI) inspections, code named DPCUI, developed by the Research and Development Center (R&DC) at Saudi Aramco in partnership with Baker Hughes. This inspection system enables fast external thermographic screening of large facilities by covering many condition monitoring locations (CML), and without any contact with the asset surfaces. It examines temporal thermography datasets that are collected using a high-resolution IR camera, such as those provided by FLIR, and on one or more RGB images that provide context of inspected areas. The collected data is analyzed by a dedicated AI engine to detect the presence of abnormal heat transfer signatures that occur due to defects present within the targeted CMLs. The novelty of this AI powered technology has several advantages. It provides a contact-less, smart, easy, fast, automated, safe, and reliable risk-based repair and maintenance decision making on the integrity of assets, enabling asset owners to efficiently prioritize their operations and processes in a seamless manner while the assets are kept online. Here, we enhance and extend the performance of our AI models to predict not only the presence of potential CUI or not, i.e. binary classification, but also various types of potential CUI, i.e. multi-class classification, a first of its kind.

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