The BoneXpert method for automated determination of skeletal maturity

Bone age rating is associated with a considerable variability from the human interpretation, and this is the motivation for presenting a new method for automated determination of bone age (skeletal maturity). The method, called BoneXpert, reconstructs, from radiographs of the hand, the borders of 15 bones automatically and then computes "intrinsic" bone ages for each of 13 bones (radius, ulna, and 11 short bones). Finally, it transforms the intrinsic bone ages into Greulich Pyle (GP) or Tanner Whitehouse (TW) bone age. The bone reconstruction method automatically rejects images with abnormal bone morphology or very poor image quality. From the methodological point of view, BoneXpert contains the following innovations: 1) a generative model (active appearance model) for the bone reconstruction; 2) the prediction of bone age from shape, intensity, and texture scores derived from principal component analysis; 3) the consensus bone age concept that defines bone age of each bone as the best estimate of the bone age of the other bones in the hand; 4) a common bone age model for males and females; and 5) the unified modelling of TW and GP bone age. BoneXpert is developed on 1559 images. It is validated on the Greulich Pyle atlas in the age range 2-17 years yielding an SD of 0.42 years [0.37; 0.47] 95% conf, and on 84 clinical TW-rated images yielding an SD of 0.80 years [0.68; 0.93] 95% conf. The precision of the GP bone age determination (its ability to yield the same result on a repeated radiograph) is inferred under suitable assumptions from six longitudinal series of radiographs. The result is an SD on a single determination of 0.17 years [0.13; 0.21] 95% conf.

Estimation of bone mineral density by digital X-ray radiogrammetry: theoretical background and clinical testing

A new automated radiogrammetric method to estimate bone mineral density (BMD) from a single radiograph of the hand and forearm is described. Five regions of interest in radius, ulna and the three middle metacarpal bones are identified and approximately 1800 geometrical measurements from these bones are used to obtain a BMD estimate of the distal forearm, referred to as BMDDXR (from digital X-ray radiogrammetry, DXR). The measured dimensions for each bone are the cortical thickness and the outer width, in combination with an stimate of the cortical porosity. The short-term in vivo precision of BMDDXR was observed to be 0.60% in a clinical study of 24 women and the in vitro variation over 12 different radiological clinics was found to be 1% of the young normal BMDDXR level. In a cohort of 416 women BMDDXR was found to be closely correlated with BMD at the distal forearm measured by dual-energy X-ray absoptiometry (r = 0.86, p < 0.0001) and also with BMD at the spine, total hip and femoral neck (r = 0.62, 0.69 and 0.73, respectively, p<0.0001 for all). The annual decline was estimated from the cohort to be 1.05% in the age group 55-65 years. Relative to this age-related loss, the reported short-term precision allows for monitoring intervals of 1.0 years and 1.6 years in order to detect expected age-related changes with a confidence of 80% and 95%, respectively. It is concluded that the DXR method offers a BMD estimate with a good correlation with distal forearm BMD, a low variation between geographical sites and a precision that potentially allows for relatively short observation intervals.

The use of bone age in clinical practice - part 1

This review examines the role of skeletal maturity ('bone age', BA) assessment in clinical practice. BA is mainly used in children with the following conditions: short stature (addressed in part 1 of this review), tall stature, early or late puberty, and congenital adrenal hyperplasia (all addressed in part 2). Various manual and automatic methods of BA assessment have been developed. Healthy tall children tend to have advanced BA and healthy short children tend to have delayed BA in comparison to chronological age. Growth hormone (GH) treatment of children with GH deficiency leads to a catch-up in BA that is usually appropriate for the height of the child. Response to GH is dependent on BA delay in young children with idiopathic short stature, and GH dosage appears to affect BA acceleration. In chronic renal failure, BA is delayed until puberty but then increases due to increased sensitivity of the growth plate to sex steroids, thus further impairing adult height. The assessment of BA provides an important contribution to the diagnostic workup and management of children with short stature.

Minimum Description Length shape and appearance models

The Minimum Description Length (MDL) approach to shape modelling is reviewed. It solves the point correspondence problem of selecting points on shapes defined as curves so that the points correspond across a data set. An efficient numerical implementation is presented and made available as open source Matlab code. The problems with the early MDL approaches are discussed. Finally the MDL approach is extended to an MDL Appearance Model, which is proposed as a means to perform unsupervised image segmentation.

Clinical review: An automated method for determination of bone age

CONTEXT

Bone age rating is associated with a considerable rater variability, which limits its usefulness in modern pediatric endocrinology. An automated computerized method would theoretically solve this problem but has been surprisingly difficult to establish.

EVIDENCE ACQUISITION

We review the development of automated bone age assessment and describe how the conceptual understanding of bone age rating shifted from a rule-based theory to a more intuitive and experience-based approach. The role of the CASAS system from 1992 is described. The BoneXpert system from 2008 employs deformable models of each bone to locate the bones and extracts the component of the bone appearance related to maturity in a holistic, statistical manner. Two clinical studies have been published on its accuracy, defined as the root mean square deviation from manual rating. Other studies addressed the precision of the method, defined as its ability to give the same result on a repeated x-ray, expressed as the sd on a single measurement.

EVIDENCE SYNTHESIS

The accuracy of the automated bone age determination was 0.71-0.72 yr, and the precision was 0.17-0.18 yr. More than 98.6% of the images could be analyzed. The system was validated on children with various diagnoses of short stature in the bone age range 2.5-17 yr for boys and 2-15 yr for girls.

CONCLUSION

The reviewed validation studies suggest that this automated bone age determination system has adequate accuracy, precision, and efficiency to be clinically useful.

Validation and reference values of automated bone age determination for four ethnicities

RATIONALE AND OBJECTIVES

Bone age (BA) rating is associated with a considerable rater variability, which would be eliminated with an automated computerized method. The aim of the study was to validate the BoneXpert method, an automated determination of BA, in American children of four ethnicities.

MATERIALS AND METHODS

The study is based on a publicly available database of hand x-rays of healthy children, established in a previous, National Institutes of Health-funded study. Radiographs of the left hand were recorded between 1993 and 2006 in Los Angeles, including 1100 images with two independent manual BA ratings and 280 additional images for which the manual ratings were not used. Images were evenly split between Caucasian, African American, Hispanic, and Asian children, and the age range was 0-18.99 years.

RESULTS

The automated method analyzed all images with BA >2.5 years for boys and >2 years for girls. The root-mean-square (RMS) error between the two manual ratings was 0.63 years, whereas the RMS deviation between the automated BA and the average of the two manual ratings was 0.61 years. The mean BA minus age was computed versus age for each sex and ethnicity. The largest deviation from zero was seen for Hispanic and Asian children older than 12 years, who were about 1 year advanced relative to the Greulich and Pyle standard.

CONCLUSION

The automated method can analyze images of all ethnicities within a BA range of 2.5-17 years for boys and 2-15 years for girls, and can therefore eliminate the problem with rater variability in BA rating.

Prediction of adult height based on automated determination of bone age

CONTEXT

Adult height prediction is a common procedure in pediatric endocrinology, but it is associated with a considerable variability and bias from the bone age rating.

OBJECTIVE

A new method for adult height prediction is presented, based on automated bone age determination.

METHOD

The method predicts the fraction of height left to grow from age and BoneXpert bone age. This is refined by drawing the prediction toward the population mean, or alternatively toward the height predicted from the parents' heights. Boys' body mass index and girls' height at menarche can be included optionally as predictors.

PARTICIPANTS

A total of 231 normal children from the First Zurich Longitudinal Study (1ZLS) were followed from age 5 until cessation of growth with annual x-rays of the left hand. A total of 198 normal children from the Third Zurich Longitudinal Study were used for validation.

RESULTS

The root mean square error of adult height prediction (Tanner-Whitehouse 3 method in parentheses considered as standard for accuracy) on the 1ZLS was 3.3 cm (3.5 cm) for boys aged 10-15 yr and 2.7 cm (3.1 cm; P < 0.005 for difference to Tanner-Whitehouse 3) for girls aged 8-13 yr. High body mass index before puberty negatively affected adult height of boys, independent of bone age.

CONCLUSIONS

With the new method, adult height prediction has become objective because the dependence on manual bone age rating is eliminated. The method is well-suited to analyze large studies and provide a consistent body of evidence regarding the relation between maturation, body mass, and growth across populations, conditions, and ethnicities.

A paediatric bone index derived by automated radiogrammetry

SUMMARY

Hand radiographs are obtained routinely to determine bone age of children. This paper presents a method that determines a Paediatric Bone Index automatically from such radiographs. The Paediatric Bone Index is designed to have minimal relative standard deviation (7.5%), and the precision is determined to be 1.42%.

INTRODUCTION

We present a computerised method to determine bone mass of children based on hand radiographs, including a reference database for normal Caucasian children.

METHODS

Normal Danish subjects (1,867), of ages 7-17, and 531 normal Dutch subjects of ages 5-19 were included. Historically, three different indices of bone mass have been used in radiogrammetry all based on A = piTW(1 - T/W), where T is the cortical thickness and W the bone width. The indices are the metacarpal index A/W(2), DXR-BMD = A/W, and Exton-Smith's index A/(WL), where L is the length of the bone. These indices are compared with new indices of the form A/(W(a) L(b)), and it is argued that the preferred index has minimal SD relative to the mean value at each bone age and sex. Finally, longitudinal series of X-rays of 20 Japanese children are used to derive the precision of the measurements.

RESULTS

The preferred index is A/(W(1.33) L(0.33)), which is named the Paediatric Bone Index, PBI. It has mean relative SD 7.5% and precision 1.42%.

CONCLUSIONS

As part of the BoneXpert method for automated bone age determination, our method facilitates retrospective research studies involving validation of the proposed index against fracture incidence and adult bone mineral density.

IMPROVING GENERALIZATION OF NEURAL NETWORKS THROUGH PRUNING

A technique for constructing neural network architectures with better ability to generalize is presented under the name Ockham's Razor: several networks are trained and then pruned by removing connections one by one and retraining. The networks which achieve fewest connections generalize best. The method is tested on a classification of bit strings (the contiguity problem): the optimal architecture emerges, resulting in perfect generalization. The internal representation of the network changes substantially during the retraining, and this distinguishes the method from previous pruning studies.

The use of bone age in clinical practice - part 2

If height-limiting treatment is being considered for a child with tall stature, skeletal maturity is invaluable in the selection of appropriate patients for treatment, determining appropriate age of treatment commencement, monitoring progress of treatment, and determining the expected treatment effect on adult height. In precocious puberty, bone maturation can be usefully assessed at initial diagnosis and start of treatment and at regular intervals thereafter during treatment monitoring. Together with height, bone maturation is an essential parameter for long-term treatment monitoring in congenital adrenal hyperplasia. Bone age (BA) determination in children with skeletal dysplasia is only feasible in a few disorders and estimations should be treated with caution. Radiographs of the left hand and wrist are, however, essential in the diagnosis of many skeletal disorders. Bone mineralization and measures of bone lengths, width, thickness and cortical thickness should always be evaluated in relation to a child's height and BA, especially around puberty. The use of skeletal maturity, assessed on a radiograph alone to estimate chronological age for immigration authorities or criminal courts is not recommended.

Clinical application of automated Greulich-Pyle bone age determination in children with short stature

BACKGROUND

Bone age (BA) rating is time consuming and highly rater dependent.

OBJECTIVE

To adjust the fully automated BoneXpert method to agree with the manual Greulich and Pyle BA (GP BA) ratings of five raters and to validate the accuracy for short children.

MATERIALS AND METHODS

A total of 1,097 left hand radiographs from 188 children with short stature, including growth hormone deficiency (44%) and Turner syndrome (29%) were evaluated.

RESULTS

BoneXpert rejected 14 of the 1,097 radiographs, and deviated by more than 1.9 years from the operator BA for 27 radiographs. These were rerated blindly by four operators. Of the 27 new ratings, 26 were within 1.9 years of the automatic BA values. The root mean square deviation between manual and automatic rating was 0.72 years (95% CI 0.69-0.75).

CONCLUSION

BoneXpert's ability to process 99% of images automatically without errors, and to obtain good agreement with an operator suggests that the method is efficient and reliable for short children.

Validation of a new method for automated determination of bone age in Japanese children

BoneXpert, an automated method for analysis of hand radiographs of children, has recently been developed and validated in European children. It determines Tanner-Whitehouse (TW) and Greulich Pyle (GP) bone ages (BA). The purpose of this work is to validate BoneXpert BA in Japanese children and determine the following two properties of the method: (1) The accuracy of the BA, i.e. the standard deviation from an experienced Japanese TW BA rater. (2) The precision of the BA, i.e. BoneXpert's ability to yield the same BA value on a repeated radiograph. The data consist of two studies: 185 radiographs of 22 normal children followed longitudinally from approximately 7 years to full maturity, and 284 radiographs of 22 patients with growth hormone deficiency treated with growth hormone and gonadotropin-releasing hormone analogue followed from an age of 4-11 years to almost full maturity. All radiographs were rated manually according to the TW-Japan system. BoneXpert processed all images, and the accuracy (SD) of TW-Japan BA was 0.72 years (95% CI 0.68-0.76). The precision error (SD) on a single determination of GP BA was 0.17 years (95% CI 0.15-0.19). It is concluded that BoneXpert performs as well in Japanese children as it does in Caucasian children. This study accomplishes a calibration of BoneXpert to the TW-Japan standard, which performs well for the entire BA range from 4 years up to full maturity.

Bone age assessment: automated techniques coming of age?

Bone age determination from hand radiographs is one of the oldest radiographic procedures. The first atlas was published by Poland in 1898, and to date the Greulich Pyle atlas, although it dates from 1959, is still the most commonly used method. Bone age rating is time-consuming, suffers from an unsatisfactorily high rater variability, and therefore already 25 years ago it was proposed to replace the manual rating by an automated, computerized method, a field nowadays referred to as computer-aided diagnosis (CAD). The pursuit of this goal reached a first stage of accomplishment in 1992-1996 with the presentation of several systems. However, they had limited clinical value, and efforts in CAD research were increasingly focused on lesion detection for cancer screening. It was only in 2008 that a fully-automated bone age method was presented, which appears to be clinically acceptable. In this paper we consider the requirements that should be met by an automated bone age method and review the state of the art. Integration in PACS and saving time are important factors for radiologists. But it is the validation of the methods which poses the greatest challenge, because there is no gold standard for bone age rating, and the direct comparison to manual rating is therefore not sufficient for demonstrating that manual rating can be replaced by automated rating. One needs additional studies assessing the precision of a method and its accuracy when used for adult height prediction, which serves as an objective.

Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge

PURPOSE

To investigate improvements in performance for automatic bone age estimation that can be gained through model ensembling.

MATERIALS AND METHODS

A total of 48 submissions from the 2017 RSNA Pediatric Bone Age Machine Learning Challenge were used. Participants were provided with 12 611 pediatric hand radiographs with bone ages determined by a pediatric radiologist to develop models for bone age determination. The final results were determined using a test set of 200 radiographs labeled with the weighted average of six ratings. The mean pairwise model correlation and performance of all possible model combinations for ensembles of up to 10 models using the mean absolute deviation (MAD) were evaluated. A bootstrap analysis using the 200 test radiographs was conducted to estimate the true generalization MAD.

RESULTS

The estimated generalization MAD of a single model was 4.55 months. The best-performing ensemble consisted of four models with an MAD of 3.79 months. The mean pairwise correlation of models within this ensemble was 0.47. In comparison, the lowest achievable MAD by combining the highest-ranking models based on individual scores was 3.93 months using eight models with a mean pairwise model correlation of 0.67.

CONCLUSION

Combining less-correlated, high-performing models resulted in better performance than naively combining the top-performing models. Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance.© RSNA, 2019 Supplemental material is available for this article. See also the commentary by Siegel in this issue.

Automatic determination of left- and right-hand bone age in the First Zurich Longitudinal Study

BACKGROUND/AIMS

A more advanced bone age (BA) has been reported for the left hand relative to the right hand, while another study has found no such effect. The aim was to study the average difference of automated BoneXpert BA determination (left- vs. right-hand) for normal children, examine the precision of automatic BA and provide a BA reference for normal Caucasian children.

METHODS

Radiographs of both hands (age range: 2-20 years) were digitised and analysed automatically to determine Greulich-Pyle BA, producing analysis results for 3,374 left-hand and 2,752 right-hand images.

RESULTS

Comparison of left- and right-hand BA showed no average difference (<0.07 years, 95% confidence). The SD of the differences between left and right sides was 0.25 years for boys as well as girls, implying the precision of automated Greulich-Pyle BA determination was 0.18 years or better. Greulich-Pyle BA for boys and girls were on average 0.10 and 0.21 years below the chronological age.

CONCLUSION

The left and right hand give the same BA on average and the SD between the sides is 0.25 years, indicating an excellent precision of the automated method.

Validation of automatic bone age rating in children with precocious and early puberty

BACKGROUND AND AIMS

Manual bone age (BA) rating in precocious puberty (PP) is associated with considerable rater variability. The aim was to evaluate a new method for automated Greulich and Pyle (GP) BA determination in children with PP.

METHODS

Seven hundred forty-one archived X-rays from 13 boys and 103 girls with PP or early puberty of various etiologies (age range at time of X-ray, 0.3-14.8 years; mean BA advancement, 2.3 years) were rated. Automatic rating (BoneXpert BA, or BXBA) was compared with the original manual GP rating (manual BA, or ManBA). X-rays where BXBA deviated from ManBA by more than 1.5 years were rerated by three raters, and the average was formed (ReferenceBA).

RESULTS

All 741 X-rays, except nine (three images had poor quality and six were from children with a chronological age younger than 1.5 years), were analyzed automatically. The mean difference of BXBA-ManBA was -0.19 years; the SD of the differences was 0.76 years (95% confidence interval 0.72-0.80). ReferenceBA was determined for 41 images. A discrepancy from ReferenceBA greater 1.5 years was found in four images against BXBA and in 10 images against ManBA.

CONCLUSION

Automated BA is efficient and reliable in children with PP.

Metacarpal thickness, width, length and medullary diameter in children--reference curves from the First Zürich Longitudinal Study

SUMMARY

Metacarpal thickness (T), width (W), length (L) and medullary diameter (M) were measured in 3,121 X-rays from 231 healthy Caucasian children aged 3 to 19 years and analysed for bone age, age, height, weight and gender-related characteristics, showing highly differentiated growth patterns with prepubertal dips. Reference data for the four metacarpal measures are presented.

INTRODUCTION

The aim of the study was to create and explore a reference database for metacarpal T, W, L and M in children.

METHODS

Three thousand one hundred twenty-one left-hand X-rays (1,661 from boys) from 231 healthy Caucasian subjects (119 boys) aged 3 to 19 years were analysed by BoneXpert, a programme for automatic analysis of hand X-rays and bone age (BA; in years).

RESULTS

In boys, growth of T, W and L shows a prepubertal decrease from BA 7 to 13 and then accelerates again. In girls, the same is seen only for T starting from BA 8 to 11, whereas W and L grow at a declining rate. M shows steady growth until BA 10.5 in girls and BA 13.5 in boys and then grows smaller in both. W is greater in boys from BA 6 onwards, while L is greater in girls from BA 9 to 13 and T from BA 11 to 14. BA is reflected best by L until start of puberty and by T and L thereafter.

CONCLUSION

T, W, L and M show highly differentiated growth patterns. These reference data provide a basis for further research into skeletal development and the management of hormone therapies in children.

Automated determination of bone age from hand X-rays at the end of puberty and its applicability for age estimation

The BoneXpert method for automated determination of bone age from hand X-rays was introduced in 2009, covering the Greulich-Pyle bone age ranges up to 17 years for boys and 15 years for girls. This paper presents an extension of the method up to bone age 19 years for boys and 18 years for girls. The extension was developed based on images from the First Zurich Longitudinal Study of 231 healthy children born in 1954-1956 and followed with annual X-rays of both hands until adulthood. The method was validated on two cross-sectional studies of healthy children from Rotterdam and Los Angeles. We found root mean square deviations from manual rating of 0.69 and 0.45 years in these two studies for boys in the bone age range 17-19 years. For girls, the deviations were 0.75 and 0.59 years, respectively, in the bone age range 15-18 years. It is shown how the automated bone age method can be applied to infer the age probability distribution for healthy Caucasian European males. Considering a population with age 15.0-21.0 years, the method can be used to decide whether the subject is above 18 years with a false positive rate (children classified as adults) of 10 % (95% confidence interval = 7-13%) and a false negative rate of 30 % (adults classified as children). To apply this method in other ethnicities will require a study of the average of "bone age - age" at the end of puberty, i.e. how much this population is shifted relative to the Greulich-Pyle standard.

Automated determination of bone age in a modern chinese population

Rationale and Objective. Large studies have previously been performed to set up a Chinese bone age reference, but it has been difficult to compare the maturation of Chinese children with populations elsewhere due to the potential variability between raters in different parts of the world. We re-analysed the radiographs from a large study of normal Chinese children using an automated bone age rating method to establish a Chinese bone age reference, and to compare the tempo of maturation in the Chinese with other populations. Materials and Methods. X-rays from 2883 boys and 3143 girls aged 2–20 years from five Chinese cities, taken in 2005, were evaluated using the BoneXpert automated method. Results. Chinese children reached full maturity at the same age as previously studied Asian children from Los Angeles, but 0.6 years earlier than Caucasian children in Los Angeles. The Greulich-Pyle bone age method was adapted to the Chinese population creating a new bone age scale BX-China05. The standard deviation between BX-China05 and chronologic age was 1.01 years in boys aged 8–14, and 1.08 years in girls aged 7–12. Conclusion. By eliminating rater variability, the automated method provides a reliable and efficient standard for bone age determination in China.

Validation of bone age methods by their ability to predict adult height

AIM

Several bone age (BA) methods are in use today. The aim of this study was to introduce a framework for assessing the validity of a BA method by its ability to predict adult height (H) and to apply it to manual ratings based on Greulich-Pyle (GP) and Tanner-Whitehouse 3 (TW) and to the fully automated BoneXpert method.

MATERIAL

The study used X-rays of 232 children from the First Zurich Longitudinal Study recorded close to each anniversary.

METHOD

For each height measurement (h), we calculated the growth potential (gp), defined as gp = (H-h)/H. The standard deviation of the gp prediction error for children of the same age was taken as a measure of the validity of the BA method and averaged over the age range 10-18 years for boys and 8-16 years for girls to obtain the overall gp prediction error (GPPE).

RESULTS

Manual TW yielded GPPE = 1.32% [95% CI 1.28-1.36], and was significantly outperformed by manual GP with GPPE = 1.26% [1.22-1.30]. The automated rating obtained GPPE = 1.23%, and omitting radius and ulna yielded GPPE = 1.22%.

CONCLUSION

Manual GP rating is better than manual TW rating in predicting adult height, and the fully automated method works as well as manual GP rating.

Automation of bone age reading and a new prediction model improve adult height prediction in children with short stature

AIM

A new method (BX AHP) for adult height prediction (AHP), based on automated bone age (BoneXpert®, here called autBA) assessment, has been developed and validated. The aim of this study was to evaluate the performance of autBA and BX AHP in comparison with manual Greulich-Pyle bone age (manBA) and Bayley-Pinneau AHP (BP AHP) in children with untreated idiopathic short stature (ISS; including familial short stature and constitutional delay of growth and puberty).

MATERIALS AND METHODS

We acquired the adult height of 190 patients (123 boys and 67 girls) with ISS and 448 (303 male and 135 female) X-rays of their left hand.

RESULTS

Mean adult height was 168 ± 6 cm for boys and 155 ± 7 cm for girls. The root mean square error of AHP using BP AHP was 6.35 cm for boys and 4.55 cm for girls. BX AHP using autBA achieved 4.71 cm for boys (p = 0.0013) and 3.72 cm for girls (0.04); including parent's height improved its root mean square error to 4.46 cm (0.0001) for boys and 3.35 cm (0.02) for girls.

CONCLUSION

The new AHP model performed significantly better than the BP AHP model. Including parental height further improved the performance of AHP.

Computational radiology in skeletal radiography

Recent years have brought rapid developments in computational image analysis in musculo-skeletal radiology. Meanwhile the algorithms have reached a maturity that makes initial clinical use feasible. Applications range from joint space measurement to erosion quantification, and from fracture detection to the assessment of alignment angles. Current results of computational image analysis in radiography are very promising, but some fundamental issues remain to be clarified, among which the definition of the optimal trade off between automatization and operator-dependency, the integration of these tools into clinical work flow and last not least the proof of incremental clinical benefit of these methods.

Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment

BACKGROUND

The autonomous artificial intelligence (AI) system for bone age rating (BoneXpert) was designed to be used in clinical radiology practice as an AI-replace tool, replacing the radiologist completely.

OBJECTIVE

The aim of this study was to investigate how the tool is used in clinical practice. Are radiologists more inclined to use BoneXpert to assist rather than replace themselves, and how much time is saved?

MATERIALS AND METHODS

We sent a survey consisting of eight multiple-choice questions to 282 radiologists in departments in Europe already using the software.

RESULTS

The 97 (34%) respondents came from 18 countries. Their answers revealed that before installing the automated method, 83 (86%) of the respondents took more than 2 min per bone age rating; this fell to 20 (21%) respondents after installation. Only 17/97 (18%) respondents used BoneXpert to completely replace the radiologist; the rest used it to assist radiologists to varying degrees. For instance, 39/97 (40%) never overruled the automated reading, while 9/97 (9%) overruled more than 5% of the automated ratings. The majority 58/97 (60%) of respondents checked the radiographs themselves to exclude features of underlying disease.

CONCLUSION

BoneXpert significantly reduces reporting times for bone age determination. However, radiographic analysis involves more than just determining bone age. It also involves identification of abnormalities, and for this reason, radiologists cannot be completely replaced. AI systems originally developed to replace the radiologist might be more suitable as AI assist tools, particularly if they have not been validated to work autonomously, including the ability to omit ratings when the image is outside the range of validity.

Metacarpal bone loss in patients with rheumatoid arthritis estimated by a new Digital X-ray Radiogrammetry method - initial results

BACKGROUND

The Digital X-ray Radiogrammetry (DXR) method measures the cortical bone thickness in the shafts of the metacarpals and has demonstrated its relevance in the assessment of hand bone loss caused by rheumatoid arthritis (RA). The aim of this study was to validate a novel approach of the DXR method in comparison with the original version considering patients with RA.

METHOD

The study includes 49 patients with verified RA. The new version is an extension of the BoneXpert method commonly used in pediatrics which has these characteristics: (1) It introduces a new technique to analyze the images which automatically validates the results for most images, and (2) it defines the measurement region relative to the ends of the metacarpals. The BoneXpert method measures the Metacarpal Index (MCI) at the metacarpal bone (II to IV). Additionally, the MCI is quantified by the DXR X-posure System.

RESULTS

The new version correctly analyzed all 49 images, and 45 were automatically validated. The standard deviation between the MCI results of the two versions was 2.9% of the mean MCI. The average Larsen score was 2.6 with a standard deviation of 1.3. The correlation of MCI to Larsen score was -0.81 in both versions, and there was no significant difference in their ability to detect erosions.

CONCLUSION

The new DXR version (BoneXpert) validated 92% of the cases automatically, while the same good correlation to RA severity could be presented compared to the old version.