Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection.

Artificial intelligence (AI) can assist dentists in image assessment, for example, caries detection. The wider health and cost impact of employing AI for dental diagnostics has not yet been evaluated. We compared the cost-effectiveness of proximal caries detection on bitewing radiographs with versus without AI. U-Net, a fully convolutional neural network, had been trained, validated, and tested on 3,293, 252, and 141 bitewing radiographs, respectively, on which 4 experienced dentists had marked carious lesions (reference test). Lesions were stratified for initial lesions (E1/E2/D1, presumed noncavitated, receiving caries infiltration if detected) and advanced lesions (D2/D3, presumed cavitated, receiving restorative care if detected). A Markov model was used to simulate the consequences of true- and false-positive and true- and false-negative detections, as well as the subsequent decisions over the lifetime of patients. A German mixed-payers perspective was adopted. Our health outcome was tooth retention years. Costs were measured in 2020 euro. Monte-Carlo microsimulations and univariate and probabilistic sensitivity analyses were conducted. The incremental cost-effectiveness ratio (ICER) and the cost-effectiveness acceptability at different willingness-to-pay thresholds were quantified. AI showed an accuracy of 0.80; dentists' mean accuracy was significantly lower at 0.71 (minimum-maximum: 0.61-0.78, P < 0.05). AI was significantly more sensitive than dentists (0.75 vs. 0.36 [0.19-0.65]; P = 0.006), while its specificity was not significantly lower (0.83 vs. 0.91 [0.69-0.98]; P > 0.05). In the base-case scenario, AI was more effective (tooth retention for a mean 64 [2.5%-97.5%: 61-65] y) and less costly (298 [244-367] euro) than assessment without AI (62 [59-64] y; 322 [257-394] euro). The ICER was -13.9 euro/y (i.e., AI saved money at higher effectiveness). In the majority (>77%) of all cases, AI was less costly and more effective. Applying AI for caries detection is likely to be cost-effective, mainly as fewer lesions remain undetected. Notably, this cost-effectiveness requires dentists to manage detected early lesions nonrestoratively.

Amalgam Alternatives: Cost-Effectiveness and Value of Information Analysis.

We aimed to assess the cost-effectiveness of amalgam alternatives-namely, incrementally placed composites (IComp), composites placed in bulk (BComp), and glass ionomer cements (GIC). In a sensitivity analysis, we also included composite inlays (CompI) and incrementally placed bulk-fills (IBComp). Moreover, the value of information (VOI) regarding the effectiveness of all strategies was determined. A mixed public-private-payer perspective in the context of Germany was adopted. Bayesian network meta-analyses were performed to yield effectiveness estimates (relative risk [RR] of failure). A 3-surfaced restoration on a permanent molar in initially 30-y-old patients was followed over patients' lifetime using a Markov model. Restorative and endodontic complications were modeled; our outcome parameter was the years of tooth retention. Costs were derived from insurance fee items. Monte Carlo microsimulations were used to estimate cost-effectiveness, cost-effectiveness acceptability, and VOI. Initially, BComp/GIC were less costly (110.11 euros) than IComp (146.82 euros) but also more prone to failures (RRs [95% credible intervals (CrI)] were 1.6 [0.8 to 3.4] for BComp and 1.3 [0.5 to 5.6] for GIC). When following patients over their lifetime, IComp was most effective (mean [SD], 41.9 [1] years) and least costly (2,076 [135] euros), hence dominating both BComp (40.5 [1] years; 2,284 [126] euros) and GIC (41.2 years; 2,177 [126] euros) in 90% of simulations. Eliminating the uncertainty around the effectiveness of the strategies was worth 3.99 euros per restoration, translating into annual economic savings of 87.8 million euros for payers. Including CompI and IBComp into our analyses had only a minimal impact, and our findings were robust in further sensitivity analyses. In conclusion, the initial savings by BComp/GIC compared with IComp are very likely to be compensated by the higher risk of failures and costs for retreatments. CompI and IBComp do not seem cost-effective. All alternatives are likely to be inferior to amalgam. The VOI was considerable, and future studies may yield significant economic benefits.

Does Classification of Composites for Network Meta-analyses Lead to Erroneous Conclusions?

OBJECTIVES: Composites can be classified differently, according to manufacturer information, filler particle size, resin-monomer base, or viscosity, for example. Using clinical trial data, network meta-analyses aim to rank different composite material classes. Dentists then use these ranks to decide whether to use specific materials. Alternatively, annual failure rates (AFRs) of materials can be assessed, not requiring any classification for synthesis. It is unclear whether different classification systems lead to different rankings of the same material (ie, erroneous conclusions). We aimed to evaluate the agreement of material rankings between different classification systems. METHODS: A systematic review was performed via MEDLINE, Cochrane Central Register of Controlled Trials, and EMBASE. Randomized controlled trials published from 2005-2015 that investigated composite restorations placed in load-bearing cavitated lesions in permanent teeth were included. Network meta-analyses were performed to rank combinations of composite classes (according to manufacturer, filler particle size, resin-monomers, viscosity) and adhesives. Material combinations were additionally ranked using AFRs. RESULTS: A total of 42 studies (6088 restorations, 2325 patients) were included. The ranking of most material class combinations showed significant agreement between classifications ( R2 ranged between 0.03 and 0.56). Comparing material combinations using AFRs had low precision and agreement with other systems. AFRs were significantly correlated with follow-up periods of trials. CONCLUSION: There was high agreement between rankings of identical materials in different classification systems. Such rankings thus allow cautious deductions as to the performance of a specific material. Syntheses based on AFRs might lead to erroneous results because AFRs are determined by follow-up periods and have low precision.

Directly Placed Restorative Materials: Review and Network Meta-analysis.

For restoring cavitated dental lesions, whether carious or not, a large number of material combinations are available. We aimed to systematically review and synthesize data of comparative dental restorative trials. A systematic review was performed. Randomized controlled trials published between 2005 and 2015 were included that compared the survival of ≥2 restorative and/or adhesive materials (i.e., no need for restorative reintervention). Pairwise and Bayesian network meta-analyses were performed, with separate evaluations for cervical cavitated lesions and load-bearing posterior cavitated lesions in permanent and primary teeth. A total of 11,070 restorations (5,330 cervical, 5,740 load bearing) had been placed in 3,633 patients in the included trials. Thirty-six trials investigated restoration of cervical lesions (all in permanent teeth) and 36 of load-bearing lesions (8 in primary and 28 in permanent teeth). Resin-modified glass ionomer cements had the highest chance of survival in cervical cavitated lesions; composites or compomers placed via 2-step self-etch and 3-step etch-and-rinse adhesives were ranked next. Restorations placed with 2-step etch-and-rinse or 1-step self-etch adhesives performed worst. For load-bearing restorations, conventional composites had the highest probability of survival, while siloranes were found least suitable. Ambiguity remains regarding which adhesive strategy to use in load-bearing cavitated lesions. Most studies showed high risk of bias, and several comparisons were prone for publication bias. If prioritized for survival, resin-modified glass ionomer cements might be recommended to restore cervical lesions. For load-bearing ones, conventional or bulk fill composites seem most suitable. The available evidence is quantitatively and qualitatively insufficient for further recommendations, especially with regard to adhesive strategies in posterior load-bearing situations. Moreover, different material classifications might yield different findings on the same materials. Future trials should aim for sufficient power, longer follow-up times, and high internal validity to prove or refute differences between certain material combinations. An agreed material classification for future syntheses is desirable.

Effect of Industry Sponsorship on Dental Restorative Trials.

Industry sponsorship was found to potentially introduce bias into clinical trials. We assessed the effects of industry sponsorship on the design, comparator choice, and findings of randomized controlled trials on dental restorative materials. A systematic review was performed via MEDLINE, CENTRAL, and EMBASE. Randomized trials on dental restorative and adhesive materials published 2005 to 2015 were included. The design of sponsored and nonsponsored trials was compared statistically (risk of bias, treatment indication, setting, transferability, sample size). Comparator choice and network geometry of sponsored and nonsponsored trials were assessed via network analysis. Material performance rankings in different trial types were estimated via Bayesian network meta-analysis. Overall, 114 studies were included (15,321 restorations in 5,232 patients). We found 21 and 41 (18% and 36%) trials being clearly or possibly industry sponsored, respectively. Trial design of sponsored and nonsponsored trials did not significantly differ for most assessed items. Sponsored trials evaluated restorations of load-bearing cavities significantly more often than nonsponsored trials, had longer follow-up periods, and showed significantly increased risk of detection bias. Regardless of sponsorship status, comparisons were mainly performed within material classes. The proportion of trials comparing against gold standard restorative or adhesive materials did not differ between trial types. If ranked for performance according to the need to re-treat (best: least re-treatments), most material combinations were ranked similarly in sponsored and nonsponsored trials. The effect of industry sponsorship on dental restorative trials seems limited.