Visual and radiographic caries detection: a tailored meta-analysis for two different settings, Egypt and Germany

Schwendicke, Falk; Elhennawy, Karim; Shahawy, Osama El; Maher, Reham; Gimenez, Thaís; Mendes, Fausto Medeiros; Willis, Brian H

doi:10.1186/s12903-018-0561-z

Cited by 3 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…False positives (FP), spectra incorrectly classified as stained (0); 4. False negatives (FN), spectra incorrectly classified as stained lesions (1).…”

Section: Occlusalmentioning

confidence: 99%

Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

et al. 2022

View full text Add to dashboard Cite

Stains and stained incipient lesions can be challenging to differentiate with established clinical tools. New diagnostic techniques are required for improved distinction to enable early noninvasive treatment. This in vitro study evaluates the performance of artificial intelligence (AI)-based classification of hyperspectral imaging data for early occlusal lesion detection and differentiation from stains. Sixty-five extracted permanent human maxillary and mandibular bicuspids and molars (International Caries Detection and Assessment System [ICDAS] II 0–4) were imaged with a hyperspectral camera (Diaspective Vision TIVITA® Tissue, Diaspective Vision, Pepelow, Germany) at a distance of 350 mm, acquiring spatial and spectral information in the wavelength range 505–1000 nm; 650 fissural spectra were used to train classification algorithms (models) for automated distinction between stained but sound enamel and stained lesions. Stratified 10-fold cross-validation was used. The model with the highest classification performance, a fine k-nearest neighbor classification algorithm, was used to classify five additional tooth fissural areas. Polarization microscopy of ground sections served as reference. Compared to stained lesions, stained intact enamel showed higher reflectance in the wavelength range 525–710 nm but lower reflectance in the wavelength range 710–1000 nm. A fine k-nearest neighbor classification algorithm achieved the highest performance with a Matthews correlation coefficient (MCC) of 0.75, a sensitivity of 0.95 and a specificity of 0.80 when distinguishing between intact stained and stained lesion spectra. The superposition of color-coded classification results on further tooth occlusal projections enabled qualitative assessment of the entire fissure’s enamel health. AI-based evaluation of hyperspectral images is highly promising as a complementary method to visual and radiographic examination for early occlusal lesion detection.

show abstract

“…False positives (FP), spectra incorrectly classified as stained (0); 4. False negatives (FN), spectra incorrectly classified as stained lesions (1).…”

Section: Occlusalmentioning

confidence: 99%

Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In dentistry, they have been employed to evaluate water fluoride coverage ( Curiel et al 2020 ), geographic incentives affecting providers’ decision making ( Ghoneim et al 2020 ), or the accessibility of services ( Eke et al 2019 ), for example. Provider-level data have been suggested to support the tailored application of diagnostic tools for caries detection, reflecting the individual disease spectrum and provider-related test conduct and application thresholds ( Schwendicke et al 2018 ).…”

Section: Data-enriched Clinical Carementioning

confidence: 99%

Data Dentistry: How Data Are Changing Clinical Care and Research

Schwendicke

Krois

2021

J Dent Res

View full text Add to dashboard Cite

Data are a key resource for modern societies and expected to improve quality, accessibility, affordability, safety, and equity of health care. Dental care and research are currently transforming into what we term data dentistry, with 3 main applications: 1) medical data analysis uses deep learning, allowing one to master unprecedented amounts of data (language, speech, imagery) and put them to productive use. 2) Data-enriched clinical care integrates data from individual (e.g., demographic, social, clinical and omics data, consumer data), setting (e.g., geospatial, environmental, provider-related data), and systems level (payer or regulatory data to characterize input, throughput, output, and outcomes of health care) to provide a comprehensive and continuous real-time assessment of biologic perturbations, individual behaviors, and context. Such care may contribute to a deeper understanding of health and disease and a more precise, personalized, predictive, and preventive care. 3) Data for research include open research data and data sharing, allowing one to appraise, benchmark, pool, replicate, and reuse data. Concerns and confidence into data-driven applications, stakeholders’ and system’s capabilities, and lack of data standardization and harmonization currently limit the development and implementation of data dentistry. Aspects of bias and data-user interaction require attention. Action items for the dental community circle around increasing data availability, refinement, and usage; demonstrating safety, value, and usefulness of applications; educating the dental workforce and consumers; providing performant and standardized infrastructure and processes; and incentivizing and adopting open data and data sharing.

show abstract

“…Caries detection and diagnostic studies aim to assess the accuracy of a diagnostic method, or combinations thereof, with commonly used statistical measures such as sensitivity and specificity, or the reliability of a diagnostic workup or both. Systematic reviews indicate, however, that the quality of reporting in caries detection and diagnostic studies is frequently low [3][4][5][6][7][8][9]. Incomplete and inconsistent reporting can preclude a comprehensive evaluation of study methods and the potential of bias.…”

Section: Introductionmentioning

confidence: 99%

“…The composition of the sample of teeth with regard to the presence and depth of caries lesions can vary greatly [3,5,9]. So-called spectrum effects that derive from the specific case mix in a sample have a profound impact on the diagnostic performance of a test [15].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, there is a majority of studies concentrating on the accuracy of caries lesion detection and (staging) assessment usually in an in vitro setting, but also in vivo in a selected sample of teeth [ 3 , 5 , 7 , 8 ]. The composition of the sample of teeth with regard to the presence and depth of caries lesions can vary greatly [ 3 , 5 , 9 ]. So-called spectrum effects that derive from the specific case mix in a sample have a profound impact on the diagnostic performance of a test [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

STAndard Reporting of CAries Detection and Diagnostic Studies (STARCARDDS)

et al. 2021

View full text Add to dashboard Cite

Aim The aim of this paper is to present recommendations from an international workshop which evaluated the methodology and reporting of caries diagnostic studies. As a unique feature, this type of studies is focused on caries lesion detection and assessment, and many of them are carried out in vitro, because of the possibility of histological validation of the whole caries spectrum. This feature is not well covered in the existing reporting STARD guideline within the EQUATOR Network. Participants and methods An international working group of 13 cariology researchers was formed. The STARD checklist was reviewed and modified for caries detection and diagnosis purposes, in a three-step process of evaluation, consensual modification, and delivery during three 2-day workshops over 18 months. Special attention was paid to reporting requirements of caries studies that solely focus on reliability. Results The STARD checklist was modified in 14/30 items, with an emphasis on issues of sample selection (tooth selection in in vitro studies), blinding, and detailed reporting of results. Conclusion Following STARCARDDS (STAndard Reporting of CAries Detection and Diagnostic Studies) is expected to result in complete reporting of study design and methodology in future caries diagnosis and detection experiments both in vivo and in vitro, thus allowing for better comparability of studies and higher quality of systematic reviews. Clinical relevance Standardization of caries diagnostic studies leads to a better comparability among future studies, both in vivo and in vitro.

show abstract

Visual and radiographic caries detection: a tailored meta-analysis for two different settings, Egypt and Germany

Cited by 3 publications

References 20 publications

Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

Data Dentistry: How Data Are Changing Clinical Care and Research

STAndard Reporting of CAries Detection and Diagnostic Studies (STARCARDDS)

Contact Info

Product

Resources

About