ライフサイエンスコーパス: bone age

1 timates and those of the reference standard, bone age.

2 specific craniofacial features and advanced bone age.

3 adolescents usually show delayed growth and bone age.

4 e model estimates and the reference standard bone ages.

5 de point to a primary role for osteocytes in bone aging.

6 The ability to assess bone age accurately is important and allows to diagnose

7 antiandrogens appear effective in preventing bone age advancement and virilization in boys with famil

8 anastrozole, decreased bleeding episodes and bone age advancement in girls with McCune-Albright syndr

9 Bone age among HIV(-) adolescents with a history of inhi

10 lipodystrophic mice, we observed an advanced bone age, an indirect reflection of premature bone forma

11 were applied to test the association between bone age and anthropometry, adjusting for covariates inc

12 t of using cephalometric images to determine bone age and its significance for conducting appropriate

13 Delayed bone age and seizure disorders were overrepresented in t

14 solute difference (MAD) between ground-truth bone age and the model prediction bone age was calculate

15 predictors of regional body composition were bone age and weight.

16 both genders display significantly advanced bone ages and are oftentimes hypertensive.

17 nt, and to evaluate the associations between bone age, anthropometry, and diet.

18 of breast development, and the evaluation of bone age are useful tools for the imaging confirmation o

19 d-wrist radiographs using Bjork's method and bone age assessed from cephalometric radiographs using t

20 001) Pearson's correlation was found between bone age assessed from hand-wrist radiographs using Bjor

21 th automated and manual GP-based methods for bone age assessment and provides a foundation for develo

22 lation-specific deep learning algorithms for bone age assessment in modern pediatric populations.Supp

23 trauma hand radiograph-trained deep learning bone age assessment method [TDL-BAAM]) was trained on 15

24 It seems that bone age assessment methods based on evaluating the morp

25 ogical progress in medicine and stomatology, bone age assessment methods need to be perfected in orde

26 o compare the performance of a deep-learning bone age assessment model based on hand radiographs with

27 Bone age assessment of bone age could also be made based

28 Search criteria: bone age assessment, CVM method.

29 s appears to be the most desirable method of bone age assessment.

30 y was to evaluate the correspondence between bone age assessments made from hand-wrist radiographs an

31 n (R(2) = 0.59, RMSE = 1.8 cm) compared with bone age-based methods (height velocity: R(2) = 0.32, RM

32 usion tensor imaging may perform better than bone age-based models in children for the prediction of

33 ps) and compare the prediction accuracy with bone age-based models.

34 winning DL model of the 2017 RSNA Pediatric Bone Age Challenge was retrospectively evaluated and tra

35 e age model that won the 2017 RSNA Pediatric Bone Age Challenge was retrospectively evaluated using t

36 is Challenge, (ii) Regression-RSNA Pediatric Bone Age Challenge, (iii) Binary classification-GRAZPEDW

37 Radiological Society of North America (RSNA) Bone Age Challenge, 1642 images of normal Dutch and Cali

38 e matched for weight, body mass index (BMI), bone age, chronological age, Tanner breast stage, and so

39 ean absolute differences (MADs) of predicted bone ages compared with radiologist-determined ground tr

40 The assessment of bone age comprises the basic element of orthodontic diag

41 Bone age assessment of bone age could also be made based on an analysis of a mo

42 evaluate the robustness of an award-winning bone age deep learning (DL) model to extensive variation

43 arpal bones was the least important area for bone age determination.

44 Conclusion Bone age determined using tablet-based US was lower in c

45 nclusion Although an award-winning pediatric bone age DL model generalized well to curated external i

46 e To quantify generalizability and bias in a bone age DL model measured by performance on external ve

47 ted images, there were 12 times fewer severe bone age errors than in manual ratings, suggesting that

48 t, composed of 200 examinations, the mean of bone age estimates from the clinical report and three ad

49 Results The mean difference between bone age estimates of the model and of the reviewers was

50 Adult height was measured when bone age exceeded 16 years in girls and 17 years in boys

51 eXpert method for automated determination of bone age from hand X-rays was introduced in 2009 and is

52 in Adolescence (NCANDA), and determining the bone age from X-ray images of children.

53 e (AI) regression model for determining hand bone age from X-ray radiographs.

54 The chronologic age, bone age, height, weight, body-surface area, and body-ma

55 Assessment of bone age in children with use of the Greulich and Pyle a

56 Low BMD persisted after correction for bone age in girls with Crohn's disease (lumbar spine, P

57 of transabdominal pelvic ultrasonography and bone age in identifying the onset of puberty in girls at

58 ust 2017, tablet-based US was used to assess bone age in young children within their homes in rural C

59 so exhibited high PD values but contained no bone age information, suggesting a region of vulnerabili

60 Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordin

61 al Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show

62 puberty, associated with an acceleration in bone-age maturation.

63 d bias in height prediction using DTI versus bone age methods.

64 Conclusion A deep learning (DL) bone age model generalized well to an external test set,

65 The bone age model generalized well to the external test set

66 terials and Methods In December 2021, the DL bone age model that won the 2017 RSNA Pediatric Bone Age

67 of the left hand revealed a notably advanced bone age of 15.5 years.

68 e children have reached adult height, with a bone age of at least 16 years in the boys and at least 1

69 Bone age of hand, wrist and cervical spine was assessed.

70 Bone age on hand-wrist radiographs was evaluated using t

71 for number of ossified centers (P < 0.008), bone age (P < 0.0001), and bone area (P < 0.003).

72 Bone age (P </= .05) was significantly overestimated in

73 Results Two bone age prediction algorithms were tested on hand radio

74 on of commercially available AI products for bone age prediction based on hand radiographs and lung n

75 For bone age prediction, the root mean square error (RMSE) a

76 onstrated expert-level ability for pediatric bone age prediction, they have shown poor generalizabili

77 We evaluated skeletal maturation (bone age), pubertal progression, serum testosterone leve

78 Height, weight, bone age, pubertal status, skinfold thickness, and arm c

79 anthropometric assessment, pubertal staging, bone age radiography, and BMD assessment by dual energy

80 Purpose To assess bone age using tablet-based US in young children living

81 ngs on pelvic and breast ultrasonography and bone age versus the baseline measurement of luteinizing

82 ound-truth bone age and the model prediction bone age was calculated for each set.

83 age-, and sexual maturity-based biases in DL bone age were identified.

84 Images reporting ground-truth bone age were included for study.

85 oyed a holistic approach in determining hand bone age, with the wrist area being the most important a

86 Bone age z scores (BAZs) were assigned using the standar