Heuristic neural network approach in histological sections detection of hydatidiform mole.

A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k -fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15- and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets.

Barriers and challenges in adopting Saudi telemedicine network: The perceptions of decision makers of healthcare facilities in Saudi Arabia.

Despite emerging evidence about the benefits of telemedicine, there are still many barriers and challenges to its adoption. Its adoption is often cited as a failed project because 75% of them are abandoned or 'failed outright' and this percentage increases to 90% in developing countries. The literature has clarified that there is neither one-size-fit-all framework nor best-practice solution for all ICT innovations or for all countries. Barriers and challenges in adopting and implementing one ICT innovation in a given country/organisation may not be similar - not for the same ICT innovation in another country/organisation nor for another ICT innovation in the same country/organisation. To the best of our knowledge, no comprehensive scientific study has investigated these challenges and barriers in all Healthcare Facilities (HCFs) across the Kingdom of Saudi Arabia (KSA). This research, which is undertaken based on the Saudi Telemedicine Network roadmap and in collaboration with the Saudi Ministry of Health (MOH), is aimed at identifying the principle predictive challenges and barriers in the context of the KSA, and understanding the perspective of the decision makers of each HCF type, sector, and location. Three theories are used to underpin this research: the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology-Organisation-Environment (TOE) theoretical framework, and the Evaluating Telemedicine Systems Success Model (ETSSM). This study applies a three-sequential-phase approach by using three mixed methods (i.e., literature review, interviews, and questionnaires) in order to utilise the source triangulation and the data comparison analysis technique. The findings of this study show that the top three influential barriers to adopt and implement telemedicine by the HCF decision makers are: (i) the availability of adequate sustainable financial support to implement, operate, and maintain the telemedicine system, (ii) ensuring conformity of telemedicine services with core mission, vision, needs and constraints of the HCF, and (iii) the reimbursement for telemedicine services.