Prediction of Adult Height by Machine Learning Technique

A review of model evaluation metrics for machine learning in genetics and genomics

Machine learning (ML) has shown great promise in genetics and genomics where large and complex datasets have the potential to provide insight into many aspects of disease risk, pathogenesis of genetic disorders, and prediction of health and wellbeing. However, with this possibility there is a responsibility to exercise caution against biases and inflation of results that can have harmful unintended impacts. Therefore, researchers must understand the metrics used to evaluate ML models which can influence the critical interpretation of results. In this review we provide an overview of ML metrics for clustering, classification, and regression and highlight the advantages and disadvantages of each. We also detail common pitfalls that occur during model evaluation. Finally, we provide examples of how researchers can assess and utilise the results of ML models, specifically from a genomics perspective.

Estimating Maturity Status in Elite Youth Soccer Players: Evaluation of Methods

PURPOSE

The objective of this study is to evaluate the concordance of predicted maturity status classifications (pre-, circa-, or post-peak height velocity (PHV)) relative to observed age at PHV in youth soccer players.

METHODS

Longitudinal height records for 124 male soccer players were extracted from academy records spanning the 2000 to 2022 seasons. Age at PHV for each player was estimated with the Superimposition by Translation and Rotation model. Players were classified as pre-, circa-, or post-PHV using both ±1- and ±0.5-yr criteria to define the circa-PHV interval. Maturity status was estimated with several prediction protocols: maturity offset (Mirwald, Moore-1, Moore-2), maturity ratio (Fransen), and percentage of predicted adult height (PAH%) using the Khamis-Roche and Tanner-Whitehouse 2 equations using several bands: 85% to 96%, 88% to 96%, 88% to 93%, and 90% to 93% for the circa-PHV interval, and visual evaluation of individual growth curves alone or with PAH% based on Khamis-Roche and Tanner-Whitehouse 2. Concordance of maturity status classifications based on complete growth curves and predicted estimates of maturity status was addressed with percentage agreement and Cohen's kappa.

RESULTS

Visual evaluation of the growth curves had the highest concordance (≈80%) with maturity status classifications (pre-, circa-, post-PHV) based on longitudinal data for individual players. Predicted maturity offset with the Mirwald, Moore-1, and Fransen equations misclassified about one-third to one-half of the players, whereas concordance based on PAH% varied with the band used, but not with the method of height prediction.

CONCLUSIONS

Visual assessment of the individual growth curves by an experienced assessor provides an accurate estimate of maturity status relative to PHV. Maturity offset prediction equations misclassify the majority of players, whereas PAH% provides a reasonably valid alternative.

Artificial intelligence in paediatric endocrinology: conflict or cooperation

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

Adult height prediction using the growth curve comparison method

Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth.

Combined assisted bone age assessment and adult height prediction methods in Chinese girls with early puberty: analysis of three artificial intelligence systems

BACKGROUND

The applicability and accuracy of artificial intelligence (AI)-assisted bone age assessment and adult height prediction methods in girls with early puberty are unknown.

OBJECTIVE

To analyze the performance of AI-assisted bone age assessment methods by comparing the corresponding methods for predicted adult height with actual adult height.

MATERIALS AND METHODS

This retrospective review included 726 girls with early puberty, 87 of whom had reached adult height at last follow-up. Bone age was evaluated using the Greulich-Pyle (GP), Tanner-Whitehouse (TW3-RUS) and China 05 RUS-CHN (RUS-CHN) methods. Predicted adult height was calculated using the China 05 (CH05), TW3 and Bayley-Pinneau (BP) methods.

RESULTS

We analyzed 1,663 left-hand radiographs, including 155 from girls who had reached adult height. In the 6-8- and 9-11-years age groups, bone age differences were smaller than those in the 12-14-years group; however, the differences between predicted adult height and actual adult height were larger than those in the 12-14-years group. TW3 overestimated adult height by 0.4±2.8 cm, while CH05 and BP significantly underestimated adult height by 2.9±3.6 cm and 1.3±3.8 cm, respectively. TW3 yielded the highest proportion of predicted adult height within ±5 cm of actual adult height (92.9%), with the highest correlation between predicted and actual adult heights.

CONCLUSION

The differences in measured bone ages increased with increasing bone age. However, the corresponding method for predicting adult height was more accurate when the bone age was older. TW3 might be more suitable than CH05 and BP for predicting adult height in girls with early puberty. Methods for predicting adult height should be optimized for populations of the same ethnicity and disease.

Near-Adult Heights and Adult Height Predictions Using Automated and Conventional Greulich-Pyle Bone Age Determinations in Children with Chronic Endocrine Diseases

OBJECTIVES

To validate adult height predictions (BX) using automated and Greulich-Pyle bone age determinations in children with chronic endocrine diseases.

METHODS

Heights and near-adult heights were measured in 82 patients (48 females) with chronic endocrinopathies at the age of 10.45 ± 2.12 y and at time of transition to adult care (17.98 ± 3.02 y). Further, bone age (BA) was assessed using the conventional Greulich-Pyle (GP) method by three experts, and by BoneXpert™. PAH were calculated using conventional BP tables and BoneXpert™.

RESULTS

The conventional and the automated BA determinations revealed a mean difference of 0.25 ± 0.72 y (p = 0.0027). The automated PAH by BoneXpert™ were 156.26 ± 0.86 cm (SDS - 2.01 ± 1.07) in females and 171.75 ± 1.6 cm (SDS - 1.29 ± 1.06) in males, compared to 153.95 ± 1.12 cm (SDS - 2.56 ± 1.5) in females and 169.31 ± 1.6 cm (SDS - 1.66 ± 1.56) in males by conventional BP, respectively and in comparison to near-adult heights 156.38 ± 5.84 cm (SDS - 1.91 ± 1.15) in females and 168.94 ± 8.18 cm (SDS - 1.72 ± 1.22) in males, respectively.

CONCLUSION

BA ratings and adult height predictions by BoneXpert™ in children with chronic endocrinopathies abolish rater-dependent variability and enhance reproducibility of estimates thereby refining care in growth disorders. Conventional methods may outperform automated analyses in specific cases.

A Novel Method for Adult Height Prediction in Children with Idiopathic Short Stature Derived from a German-Dutch Cohort

Context

Prediction of adult height (AH) is important in clinical management of short children. The conventional methods of Bayley-Pinneau (BP) or Roche-Wainer-Thissen (RWT) have limitations.

Objective

We aimed to develop a set of algorithms for AH prediction in patients with idiopathic short stature (ISS) which are specific for combinations of predicting variables.

Methods

Demographic and auxologic data were collected in childhood (1980s) and at AH (1990s). Data were collected by Dutch and German referral centers for pediatric endocrinology. A total of 292 subjects with ISS (219 male, 73 female) were enrolled. The population was randomly split into modeling (n = 235) and validation (n = 57) cohorts. Linear multi-regression analysis was performed with predicted AH (PAH) as response variable and combinations of chronological age (CA), baseline height, parental heights, relative bone age (BA/CA), birth weight, and sex as exploratory variables.

Results

Ten models including different exploratory variables were selected with adjusted R² ranging from 0.84 to 0.78 and prediction errors from 3.16 to 3.68 cm. Applied to the validation cohort, mean residuals (PAH minus observed AH) ranged from −0.29 to −0.82 cm, while the conventional methods showed some overprediction (BP: +0.53 cm; RWT: +1.33 cm; projected AH: +3.81 cm). There was no significant trend of residuals with PAH or any exploratory variables, in contrast to BP and projected AH.

Conclusion

This set of 10 multi-regression algorithms, developed specifically for children with ISS, provides a flexible tool for AH prediction with better accuracy than the conventional methods.