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Year : 2011  |  Volume : 22  |  Issue : 2  |  Page : 362
Reliability of Logicon caries detector in the detection and depth assessment of dental caries: An in-vitro study

Department of Oral Medicine and Radiology, Sinhgad Dental College and Hospital, Pune, India

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Date of Submission23-Aug-2010
Date of Decision06-Apr-2011
Date of Acceptance01-Jun-2011
Date of Web Publication27-Aug-2011


Background: Digital radiography has so far not resulted in improved rates of proximal caries detection. Historically, automated caries detection tools have been largely academic. Opinions regarding the performance of the only such commercially available tool, viz., Logicon caries Detector (LCD) have been equivocal. This study was conducted to evaluate the reliability of LCD in the detection and depth assessment of proximal caries.
Materials and Methods: Digital images were obtained of 100 proximal tooth surfaces using the Kodak RVG 5000 sensor and analyzed by three observers. The images were then analyzed by the principal investigator using the LCD software. The teeth were then sectioned and magnified photographic images were obtained which were taken as the gold standard. All the grades were entered in proformas and the data were statistically analyzed using the chi-square test. Five parameters of reliability were calculated.
Results: The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of LCD for the grade No caries were 33, 96, 73, 82, and 81%, respectively; for the grade Enamel caries were 5, 97, 33, 80, and 79%, respectively; and for the grade Dentin caries were 100, 96, 50, 100, and 96%, respectively.
Conclusions: In conclusion, LCD appears to be more reliable in ruling out (both enamel and dentin) caries than in detecting caries.

Keywords: In-vitro , Logicon caries detector, RVG

How to cite this article:
Behere RR, Lele SM. Reliability of Logicon caries detector in the detection and depth assessment of dental caries: An in-vitro study. Indian J Dent Res 2011;22:362

How to cite this URL:
Behere RR, Lele SM. Reliability of Logicon caries detector in the detection and depth assessment of dental caries: An in-vitro study. Indian J Dent Res [serial online] 2011 [cited 2019 Oct 22];22:362. Available from:
It is agreed in literature that radiography is a more sensitive diagnostic method than mere clinical inspection for detecting proximal lesions. [1],[2],[3],[4],[5],[6] A decrease in the prevalence of dental caries and a reduction in progression rates have made the radiographic diagnostic process more challenging. Digital dental radiography, introduced in 1986 for the first time, has provided many advantages over conventional radiography. [7] Moreover, studies have shown that the diagnostic accuracy of digital imaging is comparable to film. [8],[9],[10],[11],[12] Considering the advantages that digital radiography offers, this would suggest that digital radiography be endorsed for clinical use. However, radiographic information is not always used to its full extent and improvement in the recognition of features and the cognitive use of this radiographic information in the diagnostic process could improve diagnostic accuracy. [13] This has spawned research in many areas. Image enhancement tools have been tried to make the digital images easier to interpret, but they have failed to show a significant improvement in caries diagnosis. [14],[15],[16],[17],[18] It has been postulated that the interpreter, more than the image receptor, may be the limiting factor in this diagnostic imaging chain. [17] In this regard, smart image analysis tools have been developed and evaluated by Pitts, [19] Duncan, [20] and Mileman. [21] Heaven [22] carried out a study to quantify radiographic density changes in teeth. However, such systems, developed in universities, did not lead to commercially viable systems available to the general dentist. The Logicon caries detector (LCD) software was developed by David Gakenheimer and marketed in the USA in September 1998. At present, it is the only commercially available computer-aided caries diagnosis software.

This tool has obviously been subjected to scrutiny in the past. The study carried out by Gakenheimer [23] supports its use. However, since Gakenheimer was one of the developers of the program and an employee of the manufacturer, he may have had conflicting interests in the outcome of his evaluation of the program. Other studies, however, have pointed out inconsistencies in the tool. [25],[26],[27] The latest study regarding LCD found that it did not appear to improve diagnostic ability when all carious depths were considered as a group. However, when only deeper carious lesions (inner half enamel and dentin caries) were considered, LCD appeared to improve the diagnostic ability by a significant amount. [28]

Thus, opinions regarding the reliability of LCD remain equivocal. The review of literature points to the need of resolving this uncertainty regarding the accuracy of LCD. Moreover, earlier studies regarding LCD have all used one of the following Charge Coupled Device (CCD) sensors: RVG 4, RVGui, RVG 6000 and RVG 6100. One of the earlier models in this series, RVG 5000, is still being commonly sold in India. Yet, studies regarding the reliability of LCD using this model have not been published. Further, only one study has previously estimated the reliability of LCD in the detection of enamel caries. [24]

The aim of this study was to evaluate the reliability of LCD in the detection and depth assessment of proximal caries by using the RVG 5000 sensor.

   Materials and Methods Top

The study sample consisted of 50 extracted teeth (premolars and molars) making up 100 proximal tooth surfaces. These tooth surfaces were either discolored or were apparently caries-free. Surfaces with frank cavitations and fillings were excluded. These extracted teeth were inserted in foam blocks and placed in a jig [Figure 1] that was specially prepared to standardize and maintain the focal spot-to-object distance, the object-to-image receptor distance, and the horizontal and vertical angulation of the X-ray beam. Digital radiographs were made using the Kodak RVG 5000 digital radiography system with the Satelec X-Mind AC intraoral X-ray machine (X-ray tube: Toshiba DG073B, focal spot size: 0.7 mm, exposure time range: 0.08-3.2 seconds in 17 steps, filtration: 0.8 mm of Al) operating at 70 kVp, 8 mA, 0.8 seconds and a focal spot-to-image receptor distance of 40 cm. The Kodak RVG 5000 size 1 sensor has external dimensions of 40×27 mm, active area dimensions of 30×22 mm, pixel matrix of 1200×1600, true resolution of 14 lp/mm, and is capable of capturing 4096 shades of gray. Images were acquired using the bundled Kodak Dental Imaging Software on a computer with a Pentium-4 CPU 2.4 GHz with 256 MB RAM. Images thus obtained were used for visual assessment of caries by three independent observers and for detection and depth assessment by using LCD.
Figure 1: Jig for acquisition of digital radiographic images

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The digital radiographic images were viewed by three independent observers on a 17-inch Samsung SyncMaster 793s CRT monitor (screen resolution: 1200×768 pixels, color quality: 32 bit, DPI setting: 96 dpi, refresh rate: hardware default) under fixed viewing conditions (floor-to-centre of monitor = 100 cm, centre of monitor-to-eyes = 60 cm). All observations were performed in a quiet room with subdued ambient light. The image format used was the Joint Pictures Expert Group (JPEG). No image enhancement was done. Observers were kept unaware of the lesion prevalence across the specimens.

After obtaining the digital radiographic images, each tooth was sectioned in two planes to reveal the carious lesion. Photographic images [Figure 2] were obtained of the sections using the macro mode of a Canon Powershot A620 digital camera (resolution: 7.1 megapixels, optical sensor type: CCD, focal length: 7.3-29.2 mm, lens aperture: F/2.8-4.1, macro focus range: 1-45 cm) under fixed lighting conditions, resulting in 15 times magnification, and viewed under pre-determined conditions by the same observers.
Figure 2: Magnified photographic image showing caries involving the right proximal surface

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After installation, the LCD software tool (Version 3.0.005) was accessed within the parent software (Kodak Dental Imaging Software). The LCD analysis was performed by the principal investigator as described by Gakenheimer. [23] Once the proximal surface is selected by using the V-tool [Figure 3], one of the diagnostic aids that LCD provides is the graphical representation of the density change in a tooth, by looking for a pattern of density dips starting at the tooth surface, penetrating the enamel and going into the dentin. Enamel is represented by 10 green lines and dentin by 5 blue lines. If a pattern suggestive of caries exists, the dips are highlighted with red dots to warn the dentist [Figure 4]The inventor of this software recommends the use of these density dips for extracting information from LCD because they are more relevant than the probability graph.
Figure 3: "V-tool" outlining the suspected region on the right proximal region

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Figure 4: LCD output display with high lesion probability for extension in dentin

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The observers graded absence/presence and the depth of carious lesions (seen as radiolucencies/discolorations/density dips) according to pre-determined criteria. These criteria were supplied on a chart in text as well as graphic manner. The three grades described on the chart were:

Grade 0 - No caries,

Grade 1 - Enamel caries, and

Grade 2 - Dentin caries.

The observers were asked to exclude the following two types of radiolucencies/discolorations: i) those originating near the cementoenamel junction (CEJ) and progressing apically and ii) those located entirely apical to the CEJ. Before proceeding with the analysis, both intra- and inter-observer agreements were calculated for the visual assessment of digital radiographic images and magnified photographic image (MPI) analysis. Ten digital radiographic images and 10 photographs were graded by two observers. The same 10 digital radiographic images and photographs were renamed and shown to the same observers after 8 days in a different order. These images were graded again and the new gradations were noted. The kappa test was applied. The inter-observer reliability was found to be "Good" (visual assessment: 0.7; photographs: 0.7). The two observers then discussed the reasons for disagreement, and arrived at a consensus decision regarding ambiguous cases. A third experienced observer was then calibrated and included in the study. These three observers then viewed the remaining 90 digital images and photographs and graded them according to the pre-determined criteria.

For every surface, seven assessments were obtained, i.e., one assessment each from the three observers for the digital images, one assessment each from the three observers for the MPIs, and one assessment from the results of LCD software analysis. Thus, from 50 digital radiographic images and 50 MPIs, a total of 700 assessments were obtained. All the 700 assessments for the 100 surfaces were then compiled and entered in a master chart. For arriving at a single assessment for each surface, the following criteria were used. First, a consensus, if available, was taken. If there was no consensus, the majority value (mode), if available, was taken. In the absence of a consensus or majority value, an average (mean) value of the three assessments was considered. On the basis of these criteria, a second master chart with a single assessment for each surface was prepared.


This data were then statistically analyzed. Five parameters of reliability were calculated, viz., sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and overall accuracy. The chi-square test was used to evaluate the performance of visual assessment and LCD as compared to the gold standard (MPI).

   Results Top

[Table 1] shows the agreement between LCD and the gold standard for all caries grades. Out of 76 clean surfaces, LCD was able to identify 73 surfaces correctly. Similarly, [Table 2] shows the agreement between visual assessment and the gold standard for all caries grades. Out of four cases with dentin caries, visual assessment could identify only a single case correctly. However, no other case was incorrectly over-diagnosed as dentin caries. [Table 3] compares the performance of LCD and visual assessment in terms of absolute values. The difference was found to be statistically significant only for the grade "No caries". [Table 4] compares the reliability values of LCD and visual assessment. The sensitivity, specificity, PPV, NPV, and overall accuracy of LCD for the grade "No caries" were 33, 96, 73, 82, and 81%, respectively; for the grade "Enamel caries" were 5, 97, 33, 80, and 79%, respectively; and for the grade "Dentin caries" were 100, 96, 50, 100, and 96%, respectively. The values of visual assessment for the grade "No caries" were 37, 85, 43, 82, and 74%, respectively; for the grade "Enamel caries" were 25, 82, 26, 81, and 71%, respectively; and for the grade "Dentin caries" were 25, 100, 100, 97, and 97%, respectively. Both were found to be reliable in ruling out caries. [Table 5] shows the cases where LCD analysis (unlike the visual assessment) was found to match with the gold standard. This was seen in 13 out of the total sample size of 100 surfaces. The graphical representation shown in [Figure 5] gives an impression that visual assessment is better at detecting enamel caries and LCD is better at detecting dentin caries. However, only absolute values have been considered for this graph.
Figure 5: Percentage of surfaces correctly identified by LCD and visual assessment for "No caries", "Enamel caries" and "Dentin caries"

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Table 1: Agreement between LCD and MPI for all grades

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Table 2: Agreement between visual assessment and MPI for all grades

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Table 3: Agreement between LCD and MPI as compared to the agreement between visual assessment and MPI

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Table 4: Comparison of parameters of reliability of LCD and visual assessment

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Table 5: Improvement of LCD over visual assessment

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   Discussion Top

This study was carried out to estimate the reliability of LCD software in the detection and depth assessment of proximal caries using the RVG 5000 sensor, which has not been tested with LCD so far. Studies with similar methodology have been carried out in the past, with varying results. [24],[26],[27]

Detection of caries

The low sensitivity (33%) of LCD [Table 4] indicates that it is not good at detecting caries. However, with a PPV of 73%, when LCD gives a diagnosis of caries, a dentist could safely initiate preventive or restorative measures. The high specificity (96%) of LCD indicates that it is good at ruling out caries. Moreover, with a NPV of 82%, when LCD "says" there is "No caries", a dentist could be justified in not initiating preventive or restorative measures. The reason for the high false-negative percentage (64%) [Table 4] could be the limitation of the sensor. LCD can only identify areas of demineralization when they are interpreted as darker shades of gray as compared to the surrounding region. Even though the RVG 5000 sensor is capable of capturing 4096 gray shades and has a true resolution of 14 lp/mm, the differences in gray shades were probably too subtle for LCD to identify. The most probable reason for false positives is the presence of hypoplastic pits or concavities produced by wear. [3] However, other factors such as the pixel size of the sensor used, the grayscale latitude, "bugs" in the software, the level of section of the specimen and the composite nature of the radiographic image may also result in such disagreement.

Considering the above analysis and the overall accuracy of 81%, LCD appears to be more reliable in ruling out caries than in detecting caries.

The sensitivity of 33% obtained in our study was lower than that found by Kang et al. [26] The specificity of 96% was higher than that in Wenzel's [27] study (88% by taking the mean of specificities) and Kang et al.'s study [26] i.e., 41% for the RVGui sensor. These differences could be due to varying inherent properties of the different models of sensors and the different versions of the LCD software that were used.

Detection of enamel caries

The low sensitivity (5%) of LCD [Table 4] indicates that it is not good at detecting enamel caries. Moreover, with a low PPV (33%), when LCD gives a diagnosis of enamel caries, it cannot be trusted.

The high specificity (97%) of LCD indicates that it is good at ruling out enamel caries. Moreover, with a NPV of 80%, when LCD "says" that there is no enamel caries, a dentist could be justified in not initiating preventive or restorative measures. Thus, also considering the overall accuracy value of 79%, LCD could be relied upon to rule out enamel caries.

Only Navarro et al, [24] have so far attempted to separately analyze the reliability of LCD in the detection of enamel caries, and like in our study, also found the specificity and NPV values to be much higher than the sensitivity and PPV values.

Detection of dentin caries

The high sensitivity (100%) [Table 4] indicates that LCD is always likely to detect the demineralization pattern that is suggestive of dentin caries. Dentin is heterogeneous (composed of dentinal tubules, peri-tubular, inter-tubular dentin, and reparative dentin). Thus, the radiographic density of dentin is unlikely to be uniform. This may be responsible for the poor PPV value (50%), which suggests that when LCD detects such a demineralization pattern, it is likely to be dentin caries in only half of all instances. The high specificity (96%) indicates that it is good at ruling out dentin caries [Table 4]. Thus, also considering the NPV of 100% and the overall accuracy value of 96%, LCD can be relied upon to rule out dentin caries.

Wenzel [25] and Navarro [24] found LCD to fare very poorly when it came to detecting dentin caries. The specificity for dentin caries in our study is higher than the values obtained in the studies of Kang et al., [26] Gakenheimer [23] and Navarro. [24] However, in the context of small sample size in our study (four cases of dentin caries), LCD's ability to rule out dentin caries appears to be overestimated.

When only absolute values are considered [Table 3], the difference between visual assessment and LCD for the grade "No caries" was found to be statistically significant, while those for the grades "Enamel caries" and "Dentin caries" were not found to be statistically significant. But due to the small sample size of carious surfaces (24), performance of neither of them could be considered acceptable for detection of caries. There were a total of 23 samples [Table 5] where the visual assessment and LCD grades did not match. Interestingly, in 13 of these samples, LCD assessments were found to be in agreement with the gold standard.

As LCD has not been tested with the RVG 5000 sensor before, the reasons for its performance are purely speculative. The use of a different version may also have affected the results; however, since no information on differences between the versions has yet been published, it is difficult to say exactly how. Furthermore, the internal process of the artificial neural network used by LCD for diagnosis may vary each time and/or from image to image in a way that cannot be logically explained. [29]

In conclusion, LCD appears to be more reliable in ruling out (both enamel and dentin) caries than in detecting caries.

Further improvement of this tool, particularly toward detection of incipient lesions, needs to take place, as early detection of incipient caries is important in developing countries, since the same or better restorative outcomes can be provided with lower labor costs. [30] Lastly, the performance of this tool should be compared against that of general dentists and not experienced Oral Radiologists, as was done in this study. That would be a better reflection of the performance of this software.

   References Top

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Correspondence Address:
Rohit R Behere
Department of Oral Medicine and Radiology, Sinhgad Dental College and Hospital, Pune
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0970-9290.84277

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]

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