Background. A unique software tool hasbeen developed to assist dentists in the diffi-cult task of diagnosing radiographs for prox-imal caries. The software, called LogiconCaries Detector (Northrop Grumman Infor-mation Technology, Herndon, Va.), extractsimage features and correlates them with adatabase of known caries problems. TheLogicon software was combined with thedigital radiography system Trophy RadioVisioGraphy (Trophy Radiologie,Croissy-Beaubourg, France) and its effec-tiveness was measured in a clinical study,the results of which are reported here.Methods. The manufacturer trained 18dentists in private practices and one univer-sity clinic across the United States to usethe Logicon Caries Detector software. Thedentists diagnosed 175 surfaces with poten-tial caries and adjacent teeth expected to beclean but included as control surfaces. Thedentists first did a visual diagnosis only andthen repeated the diagnosis using the soft-ware. If their final diagnosis called for it, arestoration was performed and the depth ofcaries was recorded.Results. Effectiveness was gauged by cal-culating three measures of performance—sensitivity, specificity and accuracy—fordentin caries diagnosis by each dentist bothbefore and after using Logicon CariesDetector. Sensitivity among all dentistsbefore using the Logicon software was 70.3percent and afterward was 90.5 percent, animprovement of 20.2 percent. Dentists’specificity was 88.6 percent before using thesoftware and 88.3 percent afterward, with adifference of -0.3 percent. Dentists’ accuracywas 75.6 percent before using the softwareand 88.3 percent afterward, with animprovement of 12.7 percent.Conclusions. Logicon Caries Detectorenabled dentists to find 20 percent morecases of caries penetrating into dentin thanthey were able to find without it, while notcausing them to mistreat any additionalhealthy teeth.Clinical Implications. Digital radiog-raphy and smart software like LogiconCaries Detector will improve dentists’ diag-nostic abilities and lead to better patientcare.

The efficacy of a computerized cariesdetector in intraoraldigital radiographyDAVID C. GAKENHEIMER, Ph.D.

Visually diagnosing radiographs for proximalcaries is difficult because of variations inradiographs due to exposure level, toothstructure and tooth shape, and because theeye tends to smooth out shades of gray. A

1984 study by researchers at the University of Cali-fornia Los Angeles, or UCLA, School of Dentistryshowed that dentists using film radiographs misdiag-

nosed the depth of caries up to 40 per-cent of the time and that healthy teethwere misdiagnosed as having caries upto 20 percent of the time.1 Other studieshave similarly demonstrated the diffi-culties of visually diagnosing proximalsurfaces using film radiographs.2-8

A more recent study with a digitalradiography system showed no improve-ment in caries detection over film radio-graphs by visual evaluation alone.9

Wenzel10 did a thorough job of reviewingthe long history of caries diagnosis withfilm and digital radiography systemsthrough 1997 with similar conclusions.In March 2001, the National Institutes

of Health, or NIH, published a consensus statement ondiagnosis and management of dental caries, expressinga need for advances in radiographic methods of diag-nosing noncavitated lesions and a need for both clinicaltrials and laboratory studies to evaluate the efficacy ofnew methods. The work reported in this article con-tributes to both of these needs as identified by the NIHpanel of nonadvocate, nonfederal experts after hearing anumber of presentations from prominent investigatorsin the field.11

A B S T R A C T

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Caries detectorsoftwareenabled

dentists to find20 percent

more cases ofcaries

penetratinginto dentin

than they wereable to findwithout it.

A D V A N C E S I N D E N TA L P R O D U C T S

The advent of digital radiography has broughtnew opportunities for smart software to aid thedentist. Logicon Inc. (now Northrop GrummanInformation Technology, Herndon, Va.) developeda unique software tool to assist the dentist in thetask of diagnosing proximal caries. (The LogiconCaries Detector is distributed worldwide byTrophy Radiologie, Croissy-Beaubourg, France,and in the United States by Trophy Dental Inc.,Danbury, Conn.) The tool analyzes changes inradiographic density (directly related to the toothdensity) to identify demineralized regions of thetooth and determines the probability that a car-ious lesion is present in the enamel and dentin. Agraphic interface displays an enlarged radio-graphic image outlining the potential lesion siteon the tooth in question with separate plots oftooth density variations and lesion probability.

Other researchers have developed softwaretools for caries detection that emphasize findingincipient caries in the enamel.12-23 Logicon devel-oped the first software tool to trace the cariesfrom the surface through the enamel and into thedentin, to correlate the lesion features in theenamel and the dentin, and to produce probabili-ties that enamel and dentin lesions are presentbased on a comparison with a database of knowncaries cases developed at UCLA. Logicon receiveda U.S. patent for its process24 and demonstratedits effectiveness first in a laboratory study onextracted teeth and then in a clinical study dur-ing normal patient care. Because there was nopredicate device, Logicon applied for and receivedpremarket approval, or PMA, from the U.S. Foodand Drug Administration, or FDA, for this soft-ware device.25 The results of the clinical study arepresented in this article, while the results of thelaboratory study can be found in an FDA reportavailable in print and online.25

MATERIALS AND METHODS

Hardware and software. Logicon CariesDetector runs on a Pentium (Intel Corporation,Santa Clara, Calif.) personal computer. It is astand-alone software program that has beeninterfaced with the Trophy RadioVisioGraphy, orRVG, digital radiography system (Trophy Radi-ologie). The study reported here used Trophy’sRVG-4 sensor. The dentist uses the Logicon soft-ware to inspect a potential lesion on a proximalsurface to determine whether it penetrates deepenough to deserve treatment. After the dentistdesignates the region of interest with a custom

computer mouse tool called the V-tool, the pro-gram runs automatically and produces three diag-nostic aids, as shown in Figures 1 through 3. Theprogram first finds the outer edge of the tooth andthe dentinoenamel junction, or DEJ. (The soft-ware offers a manual mode in case the dentistprefers to trace these boundaries.) The radio-graphic density variation (that is, variations inshades of gray) and, hence, the density variationof the tooth then are analyzed along contours par-alleling the tooth surface and the DEJ (Figure 1).Changes in the radiographic density are deter-mined along each contour and displayed for thedentist in a separate plot labeled “Tooth Density”(Figure 2). Ten equally spaced contours are ana-lyzed through the enamel and five in the dentin.(Figure 1 shows examples of the contours.) Theprogram then looks for a correlation in the den-sity dips that could be related to caries disease. Ifit finds such a pattern, it highlights the patternon the density plot with red dots (Figure 2), andoutlines the edges of the density dips in red onthe tooth image (Figure 1).

To calculate the probability of a lesion’s beingpresent, the program extracts density and spatialinformation about the most obvious local radiolu-cencies—features such as magnitude (darkness),area, depth of penetration and alignment in theenamel and dentin. These features are used toclassify lesions, which are then correlated withfeatures in a database of 608 images of teeth(including molars, premolars, canines andincisors) with lesions at a range of depths seen innormal practice (including caries-free, lesion con-fined to outer one-half of enamel, lesion pene-trating more than halfway through the enamelbut not into the dentin, and lesion penetratingthrough the enamel but less than halfwaythrough the dentin). The database was developedat the UCLA School of Dentistry using extractedteeth that were radiographed, then histologicallysectioned and examined under a microscope todetermine the true lesion status.25 Using mathe-matical methods developed for image analysis inthe defense industry (neural network techniques),the correlation produces the probabilities of alesion in the enamel and dentin, as shown inFigure 3. This entire process is described morefully in the patent documentation.24

Course of treatment. Using Logicon CariesDetector’s three diagnostic aids—the image withthe radiolucency outlined, the tooth density plotand the lesion probabilities—the dentist can

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decide on a course of treatment. The bar graph oflesion probabilities uses a yellow horizontal lineto indicate the decision threshold (Figure 3). Thedecision threshold is based on a 15 percent false-positive detection rate. If the probability bar for adentinal lesion is well above this decisionthreshold, the dentist is advised to considerrestorative treatment of the tooth. On the otherhand, if the probability bar is near or below thedecision threshold, the dentist is advised to waitand re-evaluate the case at a later date. The cur-rent analysis can be stored and compared with alater analysis. The dentist can change the deci-sion threshold.

When the probability bar is above the decisionthreshold for an enamel lesion alone, the decisionto treat is based more on the dentist’s judgmentthan on the software’s output. An enamel lesion,for example, may not progress or, alternatively,may recalcify. The dentist will decide on a case-by-case basis whether to perform a restoration ortreat the tooth with fluoride and re-evaluate withanother radiograph at a later date.

Dentist selection and training for clinicalstudy. Eighteen dentists from throughout theeastern and western United States, includingdentists in private practice and dentists in thefaculty group practice at UCLA, volunteered toparticipate in the study. All were general practi-

tioners. All had some experience with computers,and all the dentists in private practice had expe-rience with the digital radiography system usedin the study (Trophy RVG).

I headed the team of Logicon researchers whotrained dentists in person in the use of LogiconCaries Detector, including how to start the soft-ware with the V-tool, interpret the highlightedfeatures displayed on the radiographic image andinterpret density variation plots and probability

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Figure 1. Diagnostic output 1 from Logicon CariesDetector (Northrop Grumman Information Technology,Herndon, Va.): tooth image showing selected analysiscontours and radiolucency site. DEJ: Dentinoenamel junc-tion. Reproduced with permission of Northrop GrummanInformation Technology.

Figure 2. Diagnostic output 2 from Logicon CariesDetector (Northrop Grumman Information Technology,Herndon, Va.): tooth density change through enamel andinto dentin, with radiolucency center highlighted. Repro-duced with permission of Northrop Grumman InformationTechnology.

Figure 3. Diagnostic output 3 from Logicon CariesDetector (Northrop Grumman Information Technology,Herndon, Va.): lesion probability with decision thresholdfor 15 percent false-positive results. Reproduced with permission of Northrop Grumman Information Technology.

bar graphs. We asked dentists to analyze a set ofimages of teeth for which the lesion status wasknown, and dentists were accepted into the studyonly after they demonstrated proficiency with thesoftware. (We advised them that the analyseswere sensitive to exactly what tooth anatomy wasincluded in the region being analyzed, and torepeat the analyses so that they could understandthis sensitivity.) We reviewed accepted radio-graphic criteria for the presence of caries witheach dentist.26 We advised dentists that weexpected the software would be most helpful indetermining whether lesions penetrated thedentin. All the dentists agreed that caries pene-trating into dentin should be treated.

Patient inclusion criteria. We asked thedentists to impose no restrictions or bias onpatient selection based on race or sex. In selectingteeth for analysis, we asked dentists to place norestriction on tooth type (that is, molar, premolar,canine or incisor). But in accordance with theproduct’s indications for use and contraindica-tions, we asked the dentists to include only prox-imal surfaces on permanent teeth, and to workaround previous restorations and overlappingcontacts if possible (or avoid them if necessary).

We instructed the dentists to select proximalsurfaces potentially requiring restorative treat-ment on a first-come, first-served basis, to avoidbiasing toward any subclass of patients or toothsurfaces. Specifically, we told the dentists toinclude all cases ranging from incipient caries(caries penetrating less than halfway into theenamel) to moderate and advanced caries (pene-trating up to halfway through the dentin). Thedentists were to exclude only the obvious (severe)cases of caries where the lesion was readily seenradiographically to penetrate more than halfwaythrough the dentin. Qualifying surfaces withlesions were identified by each participating den-tist (according to the agreed-on criteria) as testsurfaces. In addition, we asked the dentists toinclude, whenever possible, a control surface witheach test surface. These were surfaces that ini-tially were interpreted (before being analyzed withLogicon Caries Detector) to be caries-free and thatwould be exposed to direct visual and physicalexamination if the test surfaces were prepared forrestoration. Generally, the control surfaces werethe surfaces adjacent to the test surfaces.

Test and evaluation conditions and datacollection. The study was performed in a normalclinical setting with dentists following their

normal procedures (with the addition that theyobtained written patient consent). Each dentistperformed an initial evaluation of test and controlsurfaces without Logicon Caries Detector. Thisincluded a visual evaluation of the radiographsfor the presence of a lesion in the enamel anddentin and an initial judgment regarding treatingor not treating the lesion. The dentists thenapplied Logicon Caries Detector to the radio-graphic images of both test and control surfacesand performed a second evaluation of these sur-faces. Treatment was based on this second evalu-ation. If a surface was not treated, then that sur-face and the associated control surface were notincluded in the study.

If a test surface was treated, then the dentistdetermined its true lesion status during cavitypreparation. We asked each dentist to record thelesion’s percentage of penetration into the enameland its depth, in millimeters, of penetration intothe dentin. The dentist assessed the control sur-face exposed during the preparation of the testsurface to be caries-free if it exhibited no signs ofcavitation or surface demineralization as indi-cated by a catch with a dental explorer. (However,in the absence of surface preparation, the possi-bility of subsurface demineralization cannot beexcluded.) If a control surface showed signs ofcavitation and the dentist chose to restore thesurface, then we asked the dentist to record thesame lesion data as for the test surface. For vali-dation purposes, the dentists took intraoralcamera images of all prepared and exposed surfaces.

Endpoint of clinical study. The endpoint ofthe study for each tooth surface was either thecompletion of the final restoration (such asamalgam restoration, crown or composite) or thedentist’s decision not to restore the tooth becauseof lack of caries in the dentin. The determinationof caries status for each surface was made once,and it was not the intention of this study to followa given tooth surface over time.

The endpoint of the study as a whole was thepoint when a sufficient sample size was reachedto obtain a statistically significant measure of theefficacy of Logicon Caries Detector in diagnosingdentinal lesions. To estimate the number of den-tists and surfaces required, the Logicon re-searchers looked at the preliminary results fromseven dentists. Each of these dentists looked atroughly 10 surfaces with an average improvementin diagnostic accuracy of about 20 percent and

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standard deviation ofapproximately 35 percent.Assuming similar resultsfrom additional dentists,each analyzing 10 sur-faces, and assuming a tdistribution for the differ-ence between means,27 weestimated that a samplesize of 10 dentists, eachanalyzing at least 10 sur-faces, would be required toestablish significance atthe 5 percent level (P = .05). Ultimately, weobtained valid data on 175surfaces from 18 dentists,each of whom analyzed anaverage of 9.7 surfaces,and this population provedto be adequate to obtain astatistically significantmeasure of efficacy.

Demographic data.The demographic data forthe clinical study are sum-marized in Tables 1 and 2.The subjects represented arange of geographic loca-tions, ages and races,while the sex distributionwas nearly equal to that inthe general population.Although the incidence ofdental caries can dependon many factors, includingdiet (which generallychanges with the patient’sage) and local water min-eral content, as well astreatment methods, the dentist’s ability to detectcaries generally is not dependent on these factors.As a result, we saw no reason to control for any ofthese demographic parameters except that, asmentioned above, all patients were to be of an agethat they had permanent teeth (because ourcaries database includes only permanent teeth).Otherwise, we specifically wanted patientstreated on a first-come, first-served basis to avoidany accidental bias due to patient selection. Ourresulting data on caries detection, in fact, showedno dependence on the demographic parameters inTables 1 and 2.

RESULTS

We obtained valid data from 18 general-practitioner dentists with 90 patients in 16 pri-vate practice offices (including three dentists inthe UCLA Faculty Group Practice). These den-tists assessed a total of 175 valid proximal toothsurfaces for the presence of lesions penetratinginto the dentin. Initially, a total of 218 tooth sur-faces were enrolled in the study, but for 27 ofthese surfaces, the treatment was not completedbecause the patient did not return for treatmentor the dentist was unable to schedule the patient

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TABLE 1

DISTRIBUTION OF DENTISTS AND PATIENTS BY GEOGRAPHICAL REGION.

NO. OF DENTISTS NO. OF PATIENTS NO. OF SURFACESU.S. REGION AND STATE

WestCaliforniaUtahIdahoWashington

SoutheastNorth CarolinaGeorgia

NortheastNew York

TOTAL

7113

41

1

18

3283

24

182

3

90

61195

42

393

6

175

TABLE 2

DISTRIBUTION OF DENTISTS AND PATIENTS BY AGE, RACE AND SEX.

NO. OF PATIENTS NO. OF SURFACESVARIABLE

1320231865302

68

2

32

15

4248

29474032119502

130

4

55

31

8590

Age Group (Years)10-1920-2930-3940-4950-5960-6970-79> 80Unreported

Race/Ethnic GroupCaucasian

(non-Hispanic)African-American

(non-Hispanic)HispanicAsianOther or unreported

SexMaleFemale

for the study. An additional 16 surfaces wereeliminated from the study because of deviationfrom the study protocol, resulting in the absenceof usable valid data.

We gauged effectiveness by calculating threemeasures of performance—sensitivity, specificityand accuracy—for dentin caries diagnosis by eachdentist both before and after using Logicon CariesDetector. As shown in Table 3, the average(mean) sensitivity among all the dentists beforethey used Logicon Caries Detector was 70.3 per-cent; after they used the software, their sensi-tivity was 90.5 percent, with a difference of 20.2percent. Considerable variation exists in the per-formance between dentists (as reflected in thelarge value of standard deviation), but weobserved a strong trend toward improvement formost dentists (as reflected in the much smallervalue of standard error of the mean).

To control for dentist variability, we performedan unweighted paired t test28 and the Wilcoxonsigned rank test29 to determine whether theobserved mean and median differences in sensi-tivity were significant. The resulting P valueswere .0357 and .0371, respectively, for the twotests. Since the P values are small, we are able toreject the null hypothesis and conclude that thereis a significant improvement in sensitivity associ-ated with the use of Logicon Caries Detector.

One cannot make thesame conclusion for speci-ficity. As shown in Table3, the average (mean)specificity for all dentistsbefore using LogiconCaries Detector was 88.6percent and after using itwas 88.3 percent, with adifference of -0.3 percent.The observed mean differ-ence has a P value of0.754 (based on the pairedt test), and the observedmedian difference has a Pvalue of .99 (based on theWilcoxon test). That is,the observed result is con-sistent with the truemean (and median) differ-ence being zero, and wecan conclude that there isno change in specificity.

Finally, as shown inTable 3, the average (mean) accuracy for all den-tists before using Logicon Caries Detector was75.6 percent and after using it was 88.3 percent,with a difference of 12.7 percent. The observedmean difference has a P value of .0428 (based onthe paired t test), and the observed median differ-ence has a P value of .0537 (based on theWilcoxon test).

Thus our observed mean and median differ-ences are significantly different from zero, and wecan conclude there is a significant improvementin diagnostic accuracy associated with the use ofLogicon Caries Detector. In view of the findings ofthis study, it is reasonable to conclude that theimprovement in accuracy is due entirely to theimprovement in sensitivity.

DISCUSSION

Evaluating radiographs for caries can be a chal-lenging problem. The advent of digital radiog-raphy makes it possible to use advanced patternrecognition methods to aid the dentist in the diag-nosis of caries problems that are difficult to see.Logicon Caries Detector was designed to accom-plish several things:dto extract the maximum information from theshades of gray in digital radiographic images,which provide more information than the humaneye can normally see (the Trophy RVG System

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TABLE 3

RESULTS OF LOGICON CARIES DETECTOR* CLINICALSTUDY.†

SCORE AT MEASUREMENTPOINT (%)

DIFFERENCE(%)

SIGNIFICANCEP VALUE

MEASURE

Before UsingLCD

After UsingLCD

70.3‡

(± SD§ 33.6)(± SEM¶ 8.1)

88.6(± SD 23.2)(± SEM 5.6)

75.6(± SD 23.8)(± SEM 5.6)

90.5(± SD 14.4)(± SEM 3.5)

88.3(± SD 25.6)(± SEM 6.2)

88.3(± SD 17.0)(± SEM 4.0)

+20.2(± SD 36.4)(± SEM 8.8)

-0.3(± SD 3.7)

(± SEM 0.9)

+12.7(± SD 24.7)(± SEM 5.8)

Mean .0357#

Median .0371**

Mean .754Median .99

Mean .0428Median .0537

Sensitivity(True Positive)

Specificity(True Negative)

Accuracy

* Logicon Caries Detector, or LCD, is manufactured by Northrop Grumman Information Technology, Herndon, Va.

† The study involved 18 dentists treating a total of 175 surfaces. The treatment criterion was caries penetration into dentin.

‡ Mean values based on performance of each dentist.§ SD: Standard deviation.¶ SEM: Standard error of the mean.# Based on unweighted paired t test.

* * Based on Wilcoxon signed rank test.

displays 256 shades of gray, as is typical for dig-ital radiographic systems, while the human eyegenerally can discern about 40 shades of gray);dto correlate the extracted image features witha database of known caries disease features;dto display the information in a format thathelps the dentist make a treatment decision andhelps in explaining the decision to the patient.

The performance of the dentists participatingin the clinical study described in this articledemonstrates that Logicon Caries Detector canhelp dentists find 20 percent more cases of cariespenetrating into the dentin without causing themto misdiagnose additional healthy teeth. Thisimprovement was found by 18 dentists whotreated 175 surfaces with difficult-to-see cariesproblems (the obvious cases of dentin penetrationwere not included in the study). We found theresults to be statistically significant usingstandard statistical tests. It would be of interestto collect additional clinical data with other sen-sors. Since the software analyzes shades of gray,there is no reason to believe that its performancewould be different, but we would like to verifythat. We also would like to apply the techniquesused in this study to other caries problems, suchas recurrent caries under existing restorations.

It is important to note that this improvementin caries detection was found in a clinical environ-ment where dentists made decisions to treat ornot treat and actually performed the treatmentwhen appropriate. A recently published labora-tory study involving use of Logicon CariesDetector concluded that the product did notimprove the observers’ ability to detect caries.30

However, that study did not involve practicingdentists treating patients in a clinical setting.Thus, those testers received no feedback on howto use the tool. Another recent laboratory studyfound significant improvement using LogiconCaries Detector over visual diagnoses alone (by afactor of three), although the sensitivity ofLogicon Caries Detector was below what isreported here (A.G. Farman, Ph.D., D.Sc., andB.C. Kang, D.D.S., Ph.D., University of Louisville,School of Dentistry, personal communication,March 2001).

CONCLUSION

The advent of digital radiography for the dentist’soffice has provided the opportunity for the devel-opment of smart image analysis tools to assist thedentist in evaluating radiographs. We have

demonstrated that Logicon Caries Detector canenable dentists to find 20 percent more cases ofcaries penetrating the dentin than they were ableto find without it, while not causing them to mis-treat any additional healthy teeth. This same toolprovides the dentist a graphic way of explainingto the patient why treatment is needed when it iscalled for. Logicon Caries Detector is the firstdiagnostic tool of its type to be demonstrated asefficacious in a clinical study under normal dentaloperating conditions and to be approved by theFDA at its highest level (normally reserved forlifesaving drugs and devices). There are manymore opportunities to develop smart software toassist dentists in evaluating radiographs. ■

Dr. Gakenheimer is a technical director, Northrop Grumman Infor-mation Technology, 222 W. Sixth St., San Pedro, Calif. 90733-0471, e-mail “dgakenheimer@northropgrumman.com”. Address reprintrequests to Dr. Gakenheimer.

The author wishes to thank the following people for their support anddedication to this project: Douglas C. Yoon, D.D.S., University of Cali-fornia Los Angeles School of Dentistry, who invented the concept ofLogicon Caries Detector while working at Logicon and who trained thedentists involved in the clinical study and collected and validated thedata; Stuart C. White, D.D.S., Ph.D., University of California LosAngeles School of Dentistry, who managed the laboratory work, helpedplan the clinical study and provided invaluable guidance on the natureand treatment of caries disease; Joseph A. Neuhaus, M.S., PrimeAdvantage, El Segundo, Calif., and Harry Chang, M.S., Viswis Inc.,Taipei, Taiwan, who developed, tested and maintained the softwarewhile at Logicon; Jeffrey A. Gornbein, Dr.P.H., University of CaliforniaLos Angeles School of Medicine, who assisted in the statistical analysisof the clinical data; Gregg Wilensky, Ph.D., Adobe Systems, San Jose,Calif., and Narbik Manukian, Ph.D., University of Southern CaliforniaInformation Sciences Institute, Marina del Rey, Calif., for their adviceon neural networks and image analysis while at Logicon; and AnitaGigliello, Northrop Grumman Information Technology, San Pedro,Calif., who provided administrative support throughout this project.

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