Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning

Ilyas, Muhammad; Ahmad, Jawad; Lawson, Alistair; Khan, Jan Sher; Tahir, Ahsen; Adeel, Ahsan; Larijani, Hadi; Kerrouche, Abdelfateh; Shaikh, M. Guftar; Buchanan, William J.; Hussain, Amir

doi:10.1007/978-3-030-39431-8_8

Cited by 1 publication

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to existing prediction models for growth (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17), curve matching only requires HSDS data and can be used at any point in time during treatment. The accuracy of the prediction is presented by showing the variability of the growth curves of the matched patients, and the results are interpretable and explainable without adding significant workload to the clinical pathway.…”

Section: Discussionmentioning

confidence: 99%

“…The most frequently used method of deriving growth prediction models has been multiple linear regression (6)(7)(8)(9)(10)(11)(12); however, the non-linear technique of empirical curve fitting (13,14), and the machine learning technique of Artificial Neural Networks have also been applied (15)(16)(17). While these models can accurately predict growth, there are several challenges in integrating them into clinical practice to guide HCPs in their decision-making.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Curve matching to predict growth in patients receiving growth hormone therapy: An interpretable & explainable method

2022

View full text Add to dashboard Cite

Curve matching may be used to predict growth outcomes using data of patients whose growth curves resemble those of a new patient with growth hormone deficiency (GHD) and those born small for gestational age (SGA). We aimed to investigate the validity of curve matching to predict growth in patients with GHD and those born SGA receiving recombinant human growth hormone (r-hGH). Height data collected between 0–48 months of treatment were extracted from the easypod™ connect ecosystem and the easypod™ connect observational study. Selected patients with height standard deviation scores (HSDS) [-4, <-1] and age [3, <16y] at start were included. The ‘Matching Database’ consisted of patients’ monthly HSDS obtained by the broken stick method and imputation. Standard deviation (SD) was obtained from the observed minus the predicted HSDS (error) based on matched patients within the ‘Matching Database’. Data were available for 3,213 patients in the ‘Matching Database’, and 2,472 patients with 16,624 HSDS measurements in the observed database. When ≥2 HSDS measurements were available, the error SD for a one-year prediction was approximately 0.2, which corresponds to 1.1 cm, 1.3 cm, and 1.5 cm at 7, 11, and 15 years of age, respectively. Indication and age at treatment start (<11 vs ≥11 years) had a small impact on the error SD, with patients born SGA and patients aged <11 years at treatment start generally having slightly lower values. We conclude that curve matching is a simple and valid technique for predicting growth in patients with GHD and those born SGA.

show abstract