INTRODUCTION: Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.
RESULTS: The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.
CONCLUSION: In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.
METHODOLOGY: An electronic search was conducted in PubMed / Medline, Scopus, Google Scholar, and Web of Science databases until January 2023 to retrieve related studies. "Root canal morphology," "Saudi Arabia," "Micro-CT," and "cone-beam computed tomography" were used as keywords. A modified version of previously published risk of bias assessment tool was used to determine the quality assessment of included studies.
RESULTS: The literature search revealed 47 studies that matched the criteria for inclusion, out of which 44 studies used cone beam computed tomography (CBCT) and three were micro-computed tomography (micro-CT) studies. According to the modified version of risk of bias assessment tool, the studies were categorized as low, moderate, and high risk of bias. A total of 47,612 samples were included which comprised of either maxillary teeth (5,412), or mandibular teeth (20,572), and mixed teeth (21,327). 265 samples were used in micro-CT studies while 47,347 teeth samples were used in CBCT studies. Among the CBCT studies, except for three, all the studies were retrospective studies. Frequently used imaging machine and software were 3D Accuitomo 170 and Morita's i-Dixel 3D imaging software respectively. Minimum and maximum voxel sizes were 75 and 300 μm, Vertucci's classification was mostly used to classify the root canal morphology of the teeth. The included micro-CT studies were in-vitro studies where SkyScan 1172 X-ray scanner was the imaging machine with pixel size ranging between 13.4 and 27.4 μm. Vertucci, Ahmed et al. and Pomeranz et al. classifications were applied to classify the root canal morphology.
CONCLUSION: This systematic review revealed wide variations in root and canal morphology of Saudi population using high resolution imaging techniques. Clinicians should be aware of the common and unusual root and canal anatomy before commencing root canal treatment. Future micro-CT studies are needed to provide additional qualitative and quantitative data presentations.
METHODOLOGY: A systematic review and meta-analysis of nutritional water productivity (NWP) and nutrient contribution (NC) of selected cereal-legume intercrop systems was conducted through literature searches in Scopus, Web of Science and ScienceDirect databases. After the assessment, only nine articles written in English that were field experiments comprising grain cereal and legume intercrop systems were retained. Using the R statistical software (version 3.6.0), paired t-tests were used to determine if differences existed between the intercrop system and the corresponding cereal monocrop for yield (Y), water productivity (WP), NC, and NWP.
RESULTS: The intercropped cereal or legume yield was 10 to 35% lower than that for the corresponding monocrop system. In most instances, intercropping cereals with legumes improved NY, NWP, and NC due to their added nutrients. Substantial improvements were observed for calcium (Ca), where NY, NWP, and NC improved by 658, 82, and 256%, respectively.
DISCUSSION: Results showed that cereal-legume intercrop systems could improve nutrient yield in water-limited environments. Promoting cereal- legume intercrops that feature nutrient-dense legume component crops could contribute toward addressing the SDGs of Zero Hunger (SDG 3), Good Health and Well-3 (SDG 2) and Responsible consumption and production (SDG 12).
METHODS: Factors associated with survival and failure were analyzed using Cox proportional hazards and discrete time conditional logistic models.
RESULTS: TDR, found in 60 (4.1%) of 1471 Asian treatment-naive patients, was one of the significant predictors of failure. Patients with TDR to >1 drug in their regimen were >3 times as likely to fail compared to no TDR.
CONCLUSIONS: TDR was associated with failure in the context of non-fully sensitive regimens. Efforts are needed to incorporate resistance testing into national treatment programs.