Near-infrared light reflection for the early detection of proximal caries in posterior teeth: an in vivo study
Dental caries have already impacted the oral health of more than one billion individuals worldwide . Without intervention in the early stages of caries development, the progression of caries may lead to oral diseases such as pulpitis, periapical disease and even tooth loss . Therefore, early detection, early diagnosis and early treatment of dental caries are particularly important. According to the location on the tooth surface, dental caries can be categorized into proximal caries and occlusal caries. In dental clinical practice, early detection and diagnosis of dental caries mainly rely on visual and probe detection. However, owing to limited visibility, proximal caries are often overlooked under direct vision, leading to a high rate of missed diagnoses for superficial proximal caries. For decades, bitewing radiography (BWR) has been the standard for detecting early proximal lesions, offering quick visual indications of caries depth, distance to the pulp, and whether adjacent teeth are affected . However, BWR has inherent limitations. Its low sensitivity in detecting early proximal caries can lead to misdiagnosis of demineralized enamel or shallow cavities. Healthy enamel surfaces and caries confined to enamel on BWR films are often actually present in the dentin. Studies report that deeper dentin caries are easier to detect via X-ray than early enamel lesions are . Tewary et al.  reported that experience in clinical practice was the most influential factor. The authors claimed that inexperienced examiners were almost four times more likely to make an incorrect assessment in the diagnosis of proximal surface caries than experienced examiners were. Furthermore, the exposure of patients to radiation during the examination process renders BWR unsuitable for certain patients, such as pregnant women and children . Optical diagnosis refers to illumination-based devices that emit light of different wavelengths into dental tissues, and the examiner observes and records the results in a variety of ways . The main methods include fiber optic transillumination (FOTI), digital fiber optic transillumination (DIFOTI) , near-infrared light reflection (NILR) , and optical coherence tomography (OCT) methods . Researchers have reported that healthy enamel is fully transmissive to near-infrared light, with no light reflection and therefore is dark; dentin is partially light transmissible and relatively white in color, appearing as a grayscale image . When proximal caries is present, the enamel receives near- infrared light that is partially scattered and eventually reflected, resulting in a whiter color [ , , ]. Digital intraoral scanning is a novel technique in which an oral scanner is used to capture real-time digital impressions of dentition and associated tissues with 3D images. Notably, some scanners use near-infrared light detection to simultaneously identify occlusal and proximal caries [ – ]. In in vitro experiments, Ntovas et al.  demonstrated that on-screen visual assessment of 3D digital dental models with tooth color or fluorescence by means of a 3D intraoral scanner (TRIOS 4, 3Shape TRIOS A/S, Copenhagen, Denmark), showed the ability to diagnose caries synchronously with clinical visual examination. Jablonski-Momeni et al.  investigated the clinical performance of the NILR system (VistaCam iX Proxi, Dürr Dental, Bietigheim-Bissingen, Germany) in comparison with BWR and concluded that NILR is comparable to BWR in detecting carious lesions. Zvi et al.  used NILR to detect early enamel lesions with 88% accuracy and caries lesions involving the dentin‒enamel junction (DEJ) with 97% accuracy. In addition, NILR detected early enamel lesions that could not be detected using BWR alone. The iTero Element 5D scanner (Align Technologies, San Jose, USA), which is an integrated intraoral dental imaging system that captures both 3D color images of teeth and reflected near infrared light images to aid in caries detection, is comfortable and easy to use [ , , , , ]. The purpose of this study was to evaluate the validity of NILR for detecting different depths of proximal caries in posterior teeth and to compare it with commonly used clinical oral examinations and BWR imaging. 

Study design The present study was a single-center, prospective clinical study conducted at the Department of Stomatology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), China. This study recruited dental students who are currently studying or interning at the Department of Dentistry, Zhejiang Provincial People’s Hospital, as subjects. Dental students who voluntarily underwent examinations or had caries treatments at the hospital were included in this research. All dental students were considered for inclusion in this study only when BWR images were necessary for the diagnostic and therapeutic use of the disease to avoid unnecessary radiation exposure. All data collection and recordings were performed anonymously, and informed consent was obtained from all patients. The inclusion criteria were dental students with chief complaints of symptoms related to dental caries, such as toothache, hot and cold dental pain, and food impaction pain. Participants with any systemic disease that may be a contraindication to invasive dental manipulation, such as severe cerebral, hepatic, pulmonary, or renal insufficiency; physical disability; other psychiatric disorders; or verbal communication disorders, were excluded. The exclusion criteria for deciduous teeth, anterior teeth, supernumerary teeth or wisdom teeth, teeth with fillings or restorations, teeth with endodontic or periapical diseases, pregnant or lactating women, people with drug addictions or drug dependence, smokers who smoked more than 10 cigarettes/day or alcoholics. Finally, a total of 26 subjects were enrolled in the study. All the subjects received standard clinical care and treatment for dental caries, and no additional radiographs were taken for the sole purpose of the study. Before the commencement of the study, all operators and researchers involved underwent a calibration meeting to standardize the research procedures. Sample size calculation Fisher’s exact test was used to estimate the sample size of the test dataset. Since each BWR image contains multiple surfaces, the number of proximal surfaces was counted in this study. N1 represents all proximal surfaces detected by X-rays, and the proportion p1 (Pp1) is the percentage of surfaces that can be detected by X-rays but cannot be detected by NILR (NILR leakage rate). N2 represents all proximal surfaces detected by NILR, and the proportion p2 (Pp2) is the percentage of surfaces that can be detected as caries by NILR but not by X-rays (NILR overdetection rate). α is the level of significance, and β is the noninferiority margin. In the G-Power software version 3.9.1.7, “Exact” was selected for the test family, “Proportions: inequality, two independent groups (Fisher’s exact test)” was selected for the statistical test, and “Type of power analysis” was selected for the type of power analysis. For the type of power analysis, “a priori: Compute required sample size - given a, power, and effect size”. The minimum sample size of 150 proximal surfaces was calculated assuming a detection rate of Pp1 = 0.1% and Pp2 = 0.02%, with a significance level of α = 0.05 and power (1-β) = 0.8. Assuming a 20% missed visiting rate, an additional 30 tooth surfaces were added, resulting in a minimum sample size of 180 tooth surfaces. The actual sample size for this study was 516 proximal surfaces of untreated posterior teeth. Ground truth determination The method to determine the ground truth of the present study referenced the approach reported by Metzger et al. , which is based on the consensus reached by two dentists (FY and LHW) regarding the presence and severity of caries, supplemented by medical history, intraoral examination, and bitewing radiographs. Both examiners, FY and LHW, have extensive clinical experience, with more than 15 years of working independently in comprehensive hospital dental departments and reading more than 5000 radiographs. Prior to the examination, plaque removal was performed on participants via dental floss or an interdental brush to eliminate the influence of plaque on the examination of noncavitated lesions. Intraoral examination was performed under direct vision in a comfortable chair position as determined by the examiner under dental light. The examiner first used a dental probe to explore the patient’s chief complaint site, followed by an examination of the adjacent surfaces of the premolars and molars in each quadrant in the order of upper right, upper left, lower left, and lower right. For BWR detection, the film is held in place via a film holder, and the patient assists in holding the film holder in place. The projection is made with the tooth, film, and X-ray bulb parallel to each other at a fixed distance and centered on the same line. Before detection, the model was placed in front of the wax-up soft tissue substitute, and the film (KaVo IP board, 30 mm × 40 mm) was placed in front of the standard aluminum step. The exposure conditions that resulted in the best dental hard tissue density and contrast were selected as the X-ray exposure conditions (exposure voltage of 65 kV, current of 7.5 mA, and exposure time of 0.64 s) for this study by a senior radiologist (TTW). The radiologist subsequently conducted intraoral scans of the subjects and was not involved in any of the evaluations for this study. The strictly parallel projection technique was adopted to image the enrolled patients with bitewing radiography. In the BWR film, the enamel is a high-density shadow, the dentin density shadow is slightly lower than that of the enamel, the pulp cavity is a low-density shadow, and a low-density shadow area is superficially visible in the enamel and dentin for caries (Fig. ). Near infrared light reflection scanning The NILR scans in this study were performed via iTero Element 5D (Align Technologies, San Jose, USA), a commonly used intraoral scanner in clinical practice that is often employed for digital modeling in orthodontics. The iTero™ Near Infrared Imaging (NIRI) technology of this device can be utilized to assist in caries detection [ , , , ]. The translucency of the tooth structure to near-infrared light (NIL, 850 nm) translates into the brightness level of the captured image; the more translucent an object is, the darker it appears. Intact enamel is translucent to NIL and appears dark, whereas disturbances (caries) in dentin and enamel are reflective, scatter NIL, and appear brighter than intact enamel. During the scanning process, NILR images from multiple angles are automatically captured and stored, which can be viewed in the viewer where different NILR images can be viewed through a magnifying glass, especially for early diagnosis of proximal caries. The near-infrared scan was independently conducted by the same radiologist (TTW) via iTero Element 5D. The radiologist has completed training and testing with an iTero intraoral scanning company and has extensive clinical experience in oral scanning. The subjects were positioned supine in the dental chair, with the radiologist determining the appropriate chair position. A dental air‒water syringe was subsequently used to dry the saliva on the tooth surface, and intraoral scanning was subsequently performed under natural light. Two general practitioners (LHS and YCZ) completed training on NILR-assisted detection of proximal caries and passed the consistency test. Upon completion of the data collection, two examiners (LHS and YCZ) independently assessed all near-infrared scanning data in a random order, evaluating the caries status of each tooth surface, including the presence and progression of dental caries. Both examiners participated in training related to NILR-assisted diagnosis of proximal caries prior to the commencement of the study. Outcome measurements The main objective of this study was to compare the sensitivity, specificity and accuracy of NILR with those of the ground truth in detecting posterior proximal caries. According to the clinical practice guidelines of the International Caries Detection and Assessment System (ICDAS) , we categorized caries on imaging into four major groups: 0, E1, E2, D1, D2, and D3. The NILR technique distinguishes teeth into those with caries involving the DEJ, those with caries without DEJ involvement, and those with caries-free teeth. In the NILR examination, we defined caries-free teeth as healthy teeth as 0 and uninvolved and DEJ-involved caries as 1 and 2, respectively. The definitions of the various types of caries identified by the two methods are presented in Table , and the typical features of such caries on the images are shown in Fig. . In the present study, we assigned 0 in the BWR findings to correspond to 0 in the NILR caries classification; E1 and E2 in the BWR to correspond to 1 in the NILR caries classification; and similarly, D1, D2 and D3 in the BWR to correspond to 2 in the NILR caries classification. First, the sensitivity, specificity and accuracy of the iTero oral scanner for diagnosing proximal caries in permanent teeth were calculated to evaluate its applicability for detecting proximal caries in permanent teeth. Statistical analysis For all the data, the accuracy, sensitivity, and specificity were calculated to assess the diagnostic capability of NILR for proximal caries in posterior teeth. All the data are subsequently grouped according to molars and premolars, and the accuracy, sensitivity, and specificity are calculated again to evaluate the diagnostic capability of NILR for proximal caries in different tooth positions. The calculations for sensitivity, specificity, and accuracy are based on the definitions of the parameters a, b, c, and d, where a represents the number of true positives, b the number of false positives, c the number of false negatives, and d the number of true negatives. Sensitivity is calculated as a/(a + c) * 100%, representing the percentage of correctly diagnosed caries-affected teeth out of the total number of caries-affected teeth (the true positive rate). Specificity, calculated as d/(b + d) * 100%, indicates the percentage of correctly identified non-caries-affected teeth out of the total number of non-caries-affected teeth (the true negative rate). Accuracy, calculated as (a + d)/(a + b + c + d) * 100%, reflects the overall agreement with the gold standard. The agreement between the results of the iTero oral scans for posterior proximal caries and the ground truth was then analyzed via SPSS 24.0 software and tested using the kappa value. The consistency of the two examiners’ (LHS and YCZ) examination results was measured via Cohen’s kappa. A kappa value lower than 0.40 indicates poor consistency; a kappa value of 0.41–0.60 indicates moderate consistency; a kappa value of 0.61–0.80 indicates high consistency; and a kappa value of 0.81-1.00 indicates complete consistency. On the basis of the above results, receiver operating characteristic (ROC) curve graphs were generated, and the area under the ROC curve (AUC) was calculated. An AUC of 0.5 indicates a lack of discriminative capability, values between 0.5 and 0.7 indicate minimal to moderate discrimination; values from 0.7 to 0.8 indicate acceptable discrimination; values ranging from 0.8 to 0.9 indicate excellent discrimination capability; and an AUC of 0.9 or higher reflects exceptional discrimination performance. 

The present study was a single-center, prospective clinical study conducted at the Department of Stomatology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), China. This study recruited dental students who are currently studying or interning at the Department of Dentistry, Zhejiang Provincial People’s Hospital, as subjects. Dental students who voluntarily underwent examinations or had caries treatments at the hospital were included in this research. All dental students were considered for inclusion in this study only when BWR images were necessary for the diagnostic and therapeutic use of the disease to avoid unnecessary radiation exposure. All data collection and recordings were performed anonymously, and informed consent was obtained from all patients. The inclusion criteria were dental students with chief complaints of symptoms related to dental caries, such as toothache, hot and cold dental pain, and food impaction pain. Participants with any systemic disease that may be a contraindication to invasive dental manipulation, such as severe cerebral, hepatic, pulmonary, or renal insufficiency; physical disability; other psychiatric disorders; or verbal communication disorders, were excluded. The exclusion criteria for deciduous teeth, anterior teeth, supernumerary teeth or wisdom teeth, teeth with fillings or restorations, teeth with endodontic or periapical diseases, pregnant or lactating women, people with drug addictions or drug dependence, smokers who smoked more than 10 cigarettes/day or alcoholics. Finally, a total of 26 subjects were enrolled in the study. All the subjects received standard clinical care and treatment for dental caries, and no additional radiographs were taken for the sole purpose of the study. Before the commencement of the study, all operators and researchers involved underwent a calibration meeting to standardize the research procedures. 

Fisher’s exact test was used to estimate the sample size of the test dataset. Since each BWR image contains multiple surfaces, the number of proximal surfaces was counted in this study. N1 represents all proximal surfaces detected by X-rays, and the proportion p1 (Pp1) is the percentage of surfaces that can be detected by X-rays but cannot be detected by NILR (NILR leakage rate). N2 represents all proximal surfaces detected by NILR, and the proportion p2 (Pp2) is the percentage of surfaces that can be detected as caries by NILR but not by X-rays (NILR overdetection rate). α is the level of significance, and β is the noninferiority margin. In the G-Power software version 3.9.1.7, “Exact” was selected for the test family, “Proportions: inequality, two independent groups (Fisher’s exact test)” was selected for the statistical test, and “Type of power analysis” was selected for the type of power analysis. For the type of power analysis, “a priori: Compute required sample size - given a, power, and effect size”. The minimum sample size of 150 proximal surfaces was calculated assuming a detection rate of Pp1 = 0.1% and Pp2 = 0.02%, with a significance level of α = 0.05 and power (1-β) = 0.8. Assuming a 20% missed visiting rate, an additional 30 tooth surfaces were added, resulting in a minimum sample size of 180 tooth surfaces. The actual sample size for this study was 516 proximal surfaces of untreated posterior teeth. 

The method to determine the ground truth of the present study referenced the approach reported by Metzger et al. , which is based on the consensus reached by two dentists (FY and LHW) regarding the presence and severity of caries, supplemented by medical history, intraoral examination, and bitewing radiographs. Both examiners, FY and LHW, have extensive clinical experience, with more than 15 years of working independently in comprehensive hospital dental departments and reading more than 5000 radiographs. Prior to the examination, plaque removal was performed on participants via dental floss or an interdental brush to eliminate the influence of plaque on the examination of noncavitated lesions. Intraoral examination was performed under direct vision in a comfortable chair position as determined by the examiner under dental light. The examiner first used a dental probe to explore the patient’s chief complaint site, followed by an examination of the adjacent surfaces of the premolars and molars in each quadrant in the order of upper right, upper left, lower left, and lower right. For BWR detection, the film is held in place via a film holder, and the patient assists in holding the film holder in place. The projection is made with the tooth, film, and X-ray bulb parallel to each other at a fixed distance and centered on the same line. Before detection, the model was placed in front of the wax-up soft tissue substitute, and the film (KaVo IP board, 30 mm × 40 mm) was placed in front of the standard aluminum step. The exposure conditions that resulted in the best dental hard tissue density and contrast were selected as the X-ray exposure conditions (exposure voltage of 65 kV, current of 7.5 mA, and exposure time of 0.64 s) for this study by a senior radiologist (TTW). The radiologist subsequently conducted intraoral scans of the subjects and was not involved in any of the evaluations for this study. The strictly parallel projection technique was adopted to image the enrolled patients with bitewing radiography. In the BWR film, the enamel is a high-density shadow, the dentin density shadow is slightly lower than that of the enamel, the pulp cavity is a low-density shadow, and a low-density shadow area is superficially visible in the enamel and dentin for caries (Fig. ). 

The NILR scans in this study were performed via iTero Element 5D (Align Technologies, San Jose, USA), a commonly used intraoral scanner in clinical practice that is often employed for digital modeling in orthodontics. The iTero™ Near Infrared Imaging (NIRI) technology of this device can be utilized to assist in caries detection [ , , , ]. The translucency of the tooth structure to near-infrared light (NIL, 850 nm) translates into the brightness level of the captured image; the more translucent an object is, the darker it appears. Intact enamel is translucent to NIL and appears dark, whereas disturbances (caries) in dentin and enamel are reflective, scatter NIL, and appear brighter than intact enamel. During the scanning process, NILR images from multiple angles are automatically captured and stored, which can be viewed in the viewer where different NILR images can be viewed through a magnifying glass, especially for early diagnosis of proximal caries. The near-infrared scan was independently conducted by the same radiologist (TTW) via iTero Element 5D. The radiologist has completed training and testing with an iTero intraoral scanning company and has extensive clinical experience in oral scanning. The subjects were positioned supine in the dental chair, with the radiologist determining the appropriate chair position. A dental air‒water syringe was subsequently used to dry the saliva on the tooth surface, and intraoral scanning was subsequently performed under natural light. Two general practitioners (LHS and YCZ) completed training on NILR-assisted detection of proximal caries and passed the consistency test. Upon completion of the data collection, two examiners (LHS and YCZ) independently assessed all near-infrared scanning data in a random order, evaluating the caries status of each tooth surface, including the presence and progression of dental caries. Both examiners participated in training related to NILR-assisted diagnosis of proximal caries prior to the commencement of the study. 

The main objective of this study was to compare the sensitivity, specificity and accuracy of NILR with those of the ground truth in detecting posterior proximal caries. According to the clinical practice guidelines of the International Caries Detection and Assessment System (ICDAS) , we categorized caries on imaging into four major groups: 0, E1, E2, D1, D2, and D3. The NILR technique distinguishes teeth into those with caries involving the DEJ, those with caries without DEJ involvement, and those with caries-free teeth. In the NILR examination, we defined caries-free teeth as healthy teeth as 0 and uninvolved and DEJ-involved caries as 1 and 2, respectively. The definitions of the various types of caries identified by the two methods are presented in Table , and the typical features of such caries on the images are shown in Fig. . In the present study, we assigned 0 in the BWR findings to correspond to 0 in the NILR caries classification; E1 and E2 in the BWR to correspond to 1 in the NILR caries classification; and similarly, D1, D2 and D3 in the BWR to correspond to 2 in the NILR caries classification. First, the sensitivity, specificity and accuracy of the iTero oral scanner for diagnosing proximal caries in permanent teeth were calculated to evaluate its applicability for detecting proximal caries in permanent teeth. 

For all the data, the accuracy, sensitivity, and specificity were calculated to assess the diagnostic capability of NILR for proximal caries in posterior teeth. All the data are subsequently grouped according to molars and premolars, and the accuracy, sensitivity, and specificity are calculated again to evaluate the diagnostic capability of NILR for proximal caries in different tooth positions. The calculations for sensitivity, specificity, and accuracy are based on the definitions of the parameters a, b, c, and d, where a represents the number of true positives, b the number of false positives, c the number of false negatives, and d the number of true negatives. Sensitivity is calculated as a/(a + c) * 100%, representing the percentage of correctly diagnosed caries-affected teeth out of the total number of caries-affected teeth (the true positive rate). Specificity, calculated as d/(b + d) * 100%, indicates the percentage of correctly identified non-caries-affected teeth out of the total number of non-caries-affected teeth (the true negative rate). Accuracy, calculated as (a + d)/(a + b + c + d) * 100%, reflects the overall agreement with the gold standard. The agreement between the results of the iTero oral scans for posterior proximal caries and the ground truth was then analyzed via SPSS 24.0 software and tested using the kappa value. The consistency of the two examiners’ (LHS and YCZ) examination results was measured via Cohen’s kappa. A kappa value lower than 0.40 indicates poor consistency; a kappa value of 0.41–0.60 indicates moderate consistency; a kappa value of 0.61–0.80 indicates high consistency; and a kappa value of 0.81-1.00 indicates complete consistency. On the basis of the above results, receiver operating characteristic (ROC) curve graphs were generated, and the area under the ROC curve (AUC) was calculated. An AUC of 0.5 indicates a lack of discriminative capability, values between 0.5 and 0.7 indicate minimal to moderate discrimination; values from 0.7 to 0.8 indicate acceptable discrimination; values ranging from 0.8 to 0.9 indicate excellent discrimination capability; and an AUC of 0.9 or higher reflects exceptional discrimination performance. 

The Cohen’s kappa value for the NILR assessment of the two examiners was 0.839, which suggests complete consistency. Descriptive data A total of 516 proximal surfaces from 26 subjects were included in the present prospective study. Among the subjects, 14 males and 12 females aged 18 to 24 years were included. Table shows the distributions of the different caries diagnostic results for the two dual diagnostic methods (NILR and the ground truth diagnostic method). Among the 516 proximal surfaces of molars or premolars detected via the ground truth method, 448 adjacent surfaces were healthy, 66 adjacent surfaces were E1 or E2, and 2 proximal surfaces were D1, D2, or D3, accounting for 86.8%, 12.8% and 0.39%, respectively, of the total surfaces. Among the 516 posterior adjacent surfaces examined by examiners 1 and 2 using the NILR method, 408 (79.1%) and 402 (77.9%) surfaces, respectively, were identified as healthy. Caries not involving the DEJ were found on 96 (18.6%) and 97 (18.8%) surfaces, respectively, whereas caries extending into the dentin were found on 12 (2.3%) and 17 (3.3%) surfaces, respectively. NILR diagnosis Using the “ground truth” established in this study as the reference standard, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false positive (FP), false negative (FN), area under the curve (AUC), and kappa value of the NILR for caries diagnosis are presented in Table . For the posterior teeth, the accuracy of NILR was 0.78, with a sensitivity of 0.44 and a specificity of 0.83. The accuracy and specificity for both premolars and molars were essentially similar to those for overall posterior teeth, while molars showed higher sensitivity (0.57) and premolars showed lower sensitivity (0.34). The overall posterior teeth, premolars, and molars presented minor differences in positive predictive values (0.28, 0.27, and 0.28, respectively) and FPs (0.17, 0.16, and 0.19, respectively). In contrast, the NPV for premolars was lower than that for overall posterior teeth (0.88 vs. 0.91), whereas for molars, it was greater (0.94 vs. 0.91). The difference in the FN was more pronounced, with premolars having a greater FN than the overall posterior teeth (0.66 vs. 0.56) and molars having a lower FN (0.43 vs. 0.56). The ROC curves for NILR caries diagnosis in overall posterior teeth, premolars, and molars are shown in Fig. . The AUC values for overall posterior teeth, premolars, and molars were 0.634 (95% CI: 0.557–0.710), 0.591 (95% CI: 0.487–0.695), and 0.689 (95% CI: 0.579–0.799), respectively. These results indicate minimal to moderate discrimination. However, the AUC for molars approached the upper limit of this range, demonstrating a greater discriminative capability than premolars, which showed a lower discriminative capability. Cohen’s kappa was used to assess the consistency between the two diagnostic methods. In the overall posterior teeth, premolars, and molars, the kappa values were 0.214, 0.164, and 0.264, respectively, all indicating low consistency. Compared with premolars (kappa value = 0.164), molars (kappa value = 0.264) demonstrated greater consistency. 

A total of 516 proximal surfaces from 26 subjects were included in the present prospective study. Among the subjects, 14 males and 12 females aged 18 to 24 years were included. Table shows the distributions of the different caries diagnostic results for the two dual diagnostic methods (NILR and the ground truth diagnostic method). Among the 516 proximal surfaces of molars or premolars detected via the ground truth method, 448 adjacent surfaces were healthy, 66 adjacent surfaces were E1 or E2, and 2 proximal surfaces were D1, D2, or D3, accounting for 86.8%, 12.8% and 0.39%, respectively, of the total surfaces. Among the 516 posterior adjacent surfaces examined by examiners 1 and 2 using the NILR method, 408 (79.1%) and 402 (77.9%) surfaces, respectively, were identified as healthy. Caries not involving the DEJ were found on 96 (18.6%) and 97 (18.8%) surfaces, respectively, whereas caries extending into the dentin were found on 12 (2.3%) and 17 (3.3%) surfaces, respectively. 

Using the “ground truth” established in this study as the reference standard, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false positive (FP), false negative (FN), area under the curve (AUC), and kappa value of the NILR for caries diagnosis are presented in Table . For the posterior teeth, the accuracy of NILR was 0.78, with a sensitivity of 0.44 and a specificity of 0.83. The accuracy and specificity for both premolars and molars were essentially similar to those for overall posterior teeth, while molars showed higher sensitivity (0.57) and premolars showed lower sensitivity (0.34). The overall posterior teeth, premolars, and molars presented minor differences in positive predictive values (0.28, 0.27, and 0.28, respectively) and FPs (0.17, 0.16, and 0.19, respectively). In contrast, the NPV for premolars was lower than that for overall posterior teeth (0.88 vs. 0.91), whereas for molars, it was greater (0.94 vs. 0.91). The difference in the FN was more pronounced, with premolars having a greater FN than the overall posterior teeth (0.66 vs. 0.56) and molars having a lower FN (0.43 vs. 0.56). The ROC curves for NILR caries diagnosis in overall posterior teeth, premolars, and molars are shown in Fig. . The AUC values for overall posterior teeth, premolars, and molars were 0.634 (95% CI: 0.557–0.710), 0.591 (95% CI: 0.487–0.695), and 0.689 (95% CI: 0.579–0.799), respectively. These results indicate minimal to moderate discrimination. However, the AUC for molars approached the upper limit of this range, demonstrating a greater discriminative capability than premolars, which showed a lower discriminative capability. Cohen’s kappa was used to assess the consistency between the two diagnostic methods. In the overall posterior teeth, premolars, and molars, the kappa values were 0.214, 0.164, and 0.264, respectively, all indicating low consistency. Compared with premolars (kappa value = 0.164), molars (kappa value = 0.264) demonstrated greater consistency. 

The present study is an in vivo study aimed at examining the diagnostic performance of NILR for detecting proximal caries in posterior teeth. The results indicated that NILR achieved an accuracy of 0.78, a sensitivity of 0.44, and a specificity of 0.83 for the overall diagnosis of proximal caries in posterior teeth. Compared with the “ground truth” established in this study, the accuracy and specificity are satisfactory, but the sensitivity is lacking. Additionally, this study measured the same parameters on premolars and molars separately and obtained similar results. Compared with that of premolars, the diagnostic performance of NILR for molars was better. In dentistry, for clinical diagnosis and treatment, BWR and visual examination are commonly used methods for caries detection. Although some studies have suggested that the BWR is the gold standard for diagnosing proximal caries in posterior teeth, consistent findings from these investigations have indicated that using the BWR for early caries detection is not advisable for identifying occlusal or proximal lesions [ – ]. Other studies have reported that the sensitivity of the BWR for detecting proximal caries in posterior teeth is less than 0.3 . This is usually due to the limitations of BWR, such as overlap of the image and low resolution. Hence, relying solely on the BWR as the single gold standard for caries detection is not advisable. In the present study, two dentists with over 15 years of clinical experience conducted intraoral examinations of the patients and, with the aid of BWR, reached a consensus on caries diagnosis to circumvent the limitations of BWR as much as possible, thereby establishing the ground truth for caries diagnosis in this study. In the present study, the sensitivity of NILR for detecting proximal caries in posterior teeth was 0.44, indicating that NILR has only a 44% probability of correctly identifying teeth that are actually affected by caries. Lederer et al.  reported a sensitivity of 0.50 for detecting early carious lesions in posterior teeth via near-infrared reflection (NIRR). Litzenburger et al.  conducted two in vitro studies to evaluate the ability of NIRR to detect proximal caries in posterior teeth, obtaining sensitivities of 0.29 and 0.33, respectively. The low sensitivity can be explained by the projection method of NIRR; dentin has a higher capacity for absorption and scattering of near-infrared light , and near-infrared light also attenuate when passing through thicker dental tissues. Consequently, once the lesion area is located at a deeper position, the ability of NILR to detect such lesions may significantly decrease, especially for proximal caries below the contact point of adjacent teeth. Correspondingly, NILR demonstrated an increasing trend of false negatives (0.56 for overall posterior teeth, 0.66 for premolars, and 0.43 for molars), indicating that the probability of proximal surfaces actually affected by caries being incorrectly diagnosed as healthy by NILR has increased. For anatomical reasons, the marginal ridges of posterior teeth appear brighter on NILR than in other areas, as shown in Fig. . Early enamel lesions present similar or slightly greater brightness levels. When these lesions have not progressed to deeper enamel or are below the interproximal contact point, they are probably mistaken by the examiner as part of the marginal ridge and are overlooked. However, such lesions can be more easily detected through clinical examination, leading to a lower incidence of false negatives. According to the explanation in Sect. , the AUC value in this study indicates minimal to moderate discrimination, which is similar to the results reported in previous studies. Litzenburger et al.  reported AUC values of 0.58 and 0.57 for NILR detection of proximal caries in posterior teeth in two consecutive in vitro studies. The present study also utilized Cohen’s kappa to assess the consistency between the NILR and the ground truth, which yielded poor agreement (kappa value = 0.214). Metzger et al.  reported similar findings in their study, where dentists assessing early enamel lesions limited to the enamel via NILR also achieved poor agreement (kappa value = 0.24). The authors of the previous study attributed these poor outcomes to the insufficient experience and training of the dentists, but we believe this might be just one of the factors. In our study, the examiners (LHS and YCZ) both had at least five years of clinical experience and had undergone extensive NILR image-reading training. As previously described, we consider the low consistency to stem from the limitations of the NILR method in diagnosing specific posterior proximal caries locations, with the low sensitivity and AUC values further substantiating this opinion. In the present study, the results of separate analyses conducted for molars and premolars were generally consistent with those obtained for overall posterior teeth. However, when sensitivity, specificity, and the AUC were considered as reference standards, the diagnostic performance of NILR for molars was better than that for premolars. We attributed this outcome to differences in anatomical structure. On the one hand, compared with molars, premolars generally have thicker enamel at the interproximal areas , making early enamel lesions contained within the enamel more difficult to detect. On the other hand, compared with molars, premolars have a greater interproximal surface bulge, not only increasing the prevalence of caries beneath the interproximal contact point but also making it more challenging for NILR directed from the occlusal direction to detect these carious lesions. Kanar et al.  reported similar results but did not analyze the reasons. Furthermore, enamel mineralization can affect the diagnostic accuracy of NILR to some extent. An enamel with a greater degree of mineralization appears brighter under near-infrared light, making carious lesions confined to the enamel more difficult to detect because of the low contrast. Since this was an in vivo investigation, the oral condition of patients during scanning can also affect NILR imaging and consequently affect examiners’ judgment of proximal caries. However, as described in Sect. , the examiners dried the tooth surfaces before oral scanning, and saliva continued to be secreted during the scanning process, reflecting some of the light and affecting image quality. Rapone et al.  previously reported the inadequately dried tooth surfaces affected the image quality of oral scanners. Compared with BWR, NILR is a nonradiative detection method, making it more suitable for populations such as pregnant women, children, and other people who are advised against undergoing radiative examinations. Another benefit of using NILR for caries detection is its potential to identify enamel cracks, as reported in previous studies . Additionally, as a necessary procedure for collecting information before orthodontic treatment with clear aligners, the NILR function of an oral scanner can aid in the detection of early enamel lesions that are difficult to detect, thereby preventing undesired caries development during long-term orthodontic treatment. 

This study also has several limitations, one of which concerns the gold standard for caries diagnosis. The gold standard for caries diagnosis is histological examination, but this is not possible in in vivo studies. Although cone-beam computed tomography (CBCT) or even micro-CT undoubtedly represent the most accurate detection methods, the present in vivo study refrained from exposing patients to CBCT in accordance with the principle of ALARA (as low as reasonably achievable). Furthermore, this study did not assess the reproducibility of the combined clinical and radiographic examinations, which may affect the final accuracy. Ultimately, the sample population of this study consisted of dental students, with an age range concentrated between 18 and 24 years, making the age composition relatively homogeneous. Future research should focus on caries detection across a broader age spectrum of patients and include repeated clinical and radiographic examinations one week later to obtain more scientific conclusions. 

Based on the limitations of this study, the iTero intraoral scanner equipped with the NILR function is an effective clinical tool for detecting proximal caries in posterior teeth; however, this method does not demonstrate superiority over traditional methods such as bitewing radiography. NILR should not be used as the sole method of caries detection in clinical settings but rather as an auxiliary tool to assist dentists in identifying early enamel lesions that are difficult to detect. 

