Background: The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose: To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods: Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results: The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion: Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

Objectives: Crohn’s disease (CD) is a condition that can occur in any part of the gastrointestinal tract, although usually forms in the colon and terminal ileum. Magnetic resonance imaging (MRI) has become a beneficial modality in the evaluation of small bowel activity. This study reports on a systematic review and meta-analysis of magnetic resonance enterography for the prediction of CD activity and evaluation of outcomes and possible complications. Methods: Following the PRISMA guidelines, a total of 25 low-risk studies on established CD were selected, based on a QUADAS-II score of ≥ 9. Results: A sensitivity of 90% was revealed in a pooled analysis of the 19 studies, with heterogeneity of χ² = 81.83 and I² of 80.3%. Also, a specificity of 89% was calculated, with heterogeneity of χ² = 65.12 and I² of 70.0%. Conclusion: It was concluded that MRI provides an effective alternative to CT enterography in the detection of small bowel activity in CD patients under supervision of radiologist for assessment of disease activity and its complications. Its advantages include the avoidance of radiation exposure and good diagnostic accuracy.

The abdominal aorta is the largest artery in the abdomen. It then bifurcates giving the two common iliac arteries. Knowing the normal abdominal aorta diameter is a basis for diagnosing abdominal aortic aneurysms (AAAs) and subsequently developing an optimal management plan. In order to diagnose AAA, one must have a reference for the normal abdominal aortic diameter that represents the anatomical variation in the population being studied. The aim of this research is to establish normal abdominal aortic diameters in the Saudi population.

The degeneration of radial tie fibres of the central meniscal layer, and thinning of its lamellar layer results in increased intensity signals on magnetic resonance imaging, making it difficult to differentiate from true meniscal tear. This study aimed to assess the rate of encountered MRI grades 1 and 2 intrasubstance meniscal changes, and to set guidelines to report these changes based on predicted clinical outcome. A systematic review approach was employed using search engines, libraries, and databases (Google Scholar, ERIC, PubMed, and Medline) to search for scholarly sources on meniscal lesions and their significance in MRI published between 1 January 2000 and 30 June 2019. It retrieved 2750 abstracts, out of which 2738 were excluded and 13 studies meeting inclusion criteria were meta-analysed. It found an association between intrasubstances meniscal changes and outcomes. It resulted that intrasubstance meniscal changes were preservable through the protective functioning of the meniscus. Other than weight gain, no other significant risk factor of developing true meniscal tears later in life was found. It is important to examine intrasubstance meniscal change when patients suffer from mechanical meniscal symptoms especially in old age.

This review analyzes the significance of artificial intelligence (AI) and deep learning (DL) approaches used in radiology in neurosurgery patients and compares AI applications with human models to determine the applicability of AI in disease diagnosis, decision-making, and outcome prediction. A systematic review was conducted from 1997 to 2020 from the PubMed (MEDLINE) database. The search strategy adhered to guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The keywords used for the literature search included “Deep learning,” “Neurosurgery,” “Artificial Intelligence,” “Brain,” “Magnetic resonance imaging-MRI Brain,” and “Machine learning.” The studies focusing on the significance of DL and comparing AI applications with radiologists or clinical experts to enhance diagnostic protocols were included, whereas non-English articles, animal studies, articles lacking full text, and publications such as commentaries, technical notes, abstracts, editorials, opinions, and letters were excluded. A total of 24 articles were included in the review. The P value was observed in 44 out of 63 outcome measures (70%), out of which in 26 out of 63 outturn measures, artificial application subset machine learning (ML) has a significant edge over clinical diagnosis (P < 0.05). The review highlights the potential impact of AI-driven advancements in clinical radiology on enhancing treatment plans for neurosurgery patients, emphasizing the benefits of early intervention, cost reduction, time-saving approaches, and judicious health-care resource utilization. The study’s limitations include potential constraints in identifying relevant literature due to the selected search scope and inclusion criteria, not including studies published outside the specified timeframe and database, and a small number of included studies. Consequently, there is a risk of overlooking innovative methodologies or ground-breaking studies contributing to a more comprehensive understanding of AI applications in neurosurgery. Furthermore, the exclusion of certain publication types, such as commentaries, and conference papers may limit the diversity of different perspectives. However, the study highlights the potential of ML in neurosurgery and the importance of addressing variability in study design, patient populations, and outcome measures in future research to enhance the applicability of AI-driven approaches in clinical practice. It is imperative to recognize and address these challenges to understand the opportunities and limitations inherent in the integration of AI in neurosurgical practice.