Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.
Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.
Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.
METHODS: The halogen lamp was replaced by a low-cost, blue light-emitting diode (LED) lamp. Using a reformulated AO solution, the staining protocol was revised to make use of a concentration gradient instead of uniform staining. To evaluate this new AO diagnostic system, a pilot field study was conducted in the Lake Victoria basin in Kenya.
RESULTS: Without staining failure, malaria infection status of about 100 samples was determined on-site per one microscopist per day, using the improved AO diagnostic system. The improved AO diagnosis had both higher overall sensitivity (46.1 vs 38.9%: p = 0.08) and specificity (99.0 vs 96.3%) than the Giemsa method (N = 1018), using PCR diagnosis as the standard.
CONCLUSIONS: Consistent AO staining of thin blood films and rapid evaluation of malaria parasitaemia with the revised protocol produced superior results relative to the Giemsa method. This AO diagnostic system can be set up easily at low cost using an ordinary light microscope. It may supplement rapid diagnostic tests currently used in clinical settings in malaria-endemic countries, and may be considered as an inexpensive tool for case surveillance in malaria-eliminating countries.
METHODS: The concordance rate, sensitivity and specificity of the duplex ddPCR assay were determined and compared to nested PCR and duplex qPCR.
RESULTS: The duplex ddPCR assay had higher analytical sensitivity (P. vivax = 10 copies/µL and P. knowlesi = 0.01 copies/µL) compared to qPCR (P. vivax = 100 copies/µL and P. knowlesi = 10 copies/µL). Moreover, the ddPCR assay had acceptable clinical sensitivity (P. vivax = 80% and P. knowlesi = 90%) and clinical specificity (P. vivax = 87.84% and P. knowlesi = 81.08%) when compared to nested PCR. Both ddPCR and qPCR detected more double infections in the samples.
CONCLUSIONS: Overall, the ddPCR assay demonstrated acceptable efficiency in detection of P. knowlesi and P. vivax, and was more sensitive than nested PCR in detecting mixed infections. However, the duplex ddPCR assay still needs optimization to improve the assay's clinical sensitivity and specificity.