Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection

Abstract

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.

Data Dentistry: How Data Are Changing Clinical Care and Research

“…On photographic imagery, the detection and classification of caries lesions, mucosal and skin lesions, and facial profiles have been performed (Schwendicke et al 2019). Only a few studies have assessed the cost-effectiveness (Schwendicke, Rossi, et al 2020) or other impacts of computer vision technologies for dental care or dental public health; their generalizability and robustness in dentistry remain uncertain (Schwendicke, Samek, et al 2020) The Gartner Hype Cycle provides a representation of the "maturity of technologies and applications," which are classified according to their availability and located along the evolutionary stages from initial trigger and inflated expectations (hype) over possible disillusionment to increasing useful adoption and productivity. A range of data-driven or related technologies had been predicted in 2010 to be available by 2020, and most of them-located on the right side-are in productive use in dentistry by now.…”

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

“… You et al (2020) studied plaque detection of deciduous teeth based on deep learning. Schwendicke et al (2020a) applied deep CNN to detect caries in near-infrared transparent (NILT) images, and they also emphasized that applying AI for caries detection is less costly and more effective ( Schwendicke et al, 2020b ). Zhang X et al developed ConvNet, which is based on CNN, to identify dental caries from oral images captured with consumer cameras.…”

Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond

“…In particular, for pixel level (segmentation) models the translation into tooth-wise metrics should be attempted to ease interpretation by medical professionals ( Cantu et al 2020 ). Also, accuracies should be translated into tangible value (health benefit, costs); the long-term effects of AI should be explored, for example using model-based extrapolations ( Schwendicke et al 2020 ). The impact of AI on the clinical workflow, on decision-making as well as its acceptability, fidelity, and maintenance should be considered.…”