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  1. Exploring the potential of artificial intelligence in paediatric dentistry: A systematic review on deep learning algorithms for dental anomaly detection
    Hartman H, Nurdin D, Akbar S, Cahyanto A, Setiawan AS
    Int J Paediatr Dent, 2024 Jan 31.
    PMID: 38297447 DOI: 10.1111/ipd.13164
    BACKGROUND: Artificial intelligence (AI) based on deep learning (DL) algorithms has shown promise in enhancing the speed and accuracy of dental anomaly detection in paediatric dentistry.

    AIM: This systematic review aimed to investigate the performance of AI systems in identifying dental anomalies in paediatric dentistry and compare it with human performance.

    DESIGN: A systematic search of Scopus, PubMed and Google Scholar was conducted from 2012 to 2022. Inclusion criteria were based on problem/patient/population, intervention/indicator, comparison and outcome scheme and specific keywords related to AI, DL, paediatric dentistry, dental anomalies, supernumerary and mesiodens. Six of 3918 initial pool articles were included, assessing nine DL sub-systems that used panoramic radiographs or cone-beam computed tomography. Article quality was assessed using QUADAS-2.

    RESULTS: Artificial intelligence systems based on DL algorithms showed promising potential in enhancing the speed and accuracy of dental anomaly detection, with an average of 85.38% accuracy and 86.61% sensitivity. Human performance, however, outperformed AI systems, achieving 95% accuracy and 99% sensitivity. Limitations included a limited number of articles and data heterogeneity.

    CONCLUSION: The potential of AI systems employing DL algorithms is highlighted in detecting dental anomalies in paediatric dentistry. Further research is needed to address limitations, explore additional anomalies and establish the broader applicability of AI in paediatric dentistry.

  2. Fulltext Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species
    Magrach A, Senior RA, Rogers A, Nurdin D, Benedick S, Laurance WF, et al.
    Proc Biol Sci, 2016 Mar 16;283(1826):20153008.
    PMID: 26936241 DOI: 10.1098/rspb.2015.3008
    Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the 'health' and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness, and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growing tree species, which could exacerbate the loss of timber value and carbon storage already associated with logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana-tree interaction networks if heavily infested trees (i.e. the most connected ones) were more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of 'extinction debt'.
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