A statistical shape modeling approach for predicting subject-specific human skull from head surface

Nguyen, Tan-Nhu; Tran, Vi Do; Nguyen, Ho Quang; Dao, Tien Tuan

doi:10.1007/s11517-020-02219-4

Cited by 10 publications

(25 citation statements)

References 57 publications

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“…All 12 articles were published between 2015 to 2022. Six studies were identified as unspecific cohort studies [ 10 , 11 , 15 , 18 , 19 , 21 ], five were retrospective cohort studies [ 12 , 14 , 16 , 17 , 20 ], and one was a case study [ 13 ]. 10 studies described approval from a relevant human ethics committee, however this was not mentioned in two articles [ 10 , 15 ].…”

Section: Resultsmentioning

confidence: 99%

“…10 studies described approval from a relevant human ethics committee, however this was not mentioned in two articles [ 10 , 15 ]. These studies predicted 3D shapes of a wide range of body regions, including the face [ 11 , 14 , 18 – 20 ], brain [ 10 , 15 ], head [ 16 , 21 ], forearm [ 12 ], aorta [ 13 ] and breast [ 17 ]. Sample sizes ranged from n = 7 to 209.…”

Section: Resultsmentioning

confidence: 99%

“…The programming language was mentioned by 58% of the included papers. Python [ 14 , 18 , 20 ], C++ [ 11 , 16 , 19 ], and MATLAB [ 11 , 18 , 20 , 21 ] were three languages used for building and running programs, with MATLAB the most prevalent. However, none of the articles published source code which would bring difficulties to open science and method replication.…”

Section: Resultsmentioning

confidence: 99%

“…3D shape prediction was used to assist with outcome visualization [ 10 , 11 , 15 , 16 , 18 , 19 ], customized surgical planning [ 11 , 14 , 18 – 20 ], communication [ 12 , 14 , 18 ], diagnosis [ 10 , 15 ], decision-making processes [ 13 , 16 ] and implant generation [ 21 ]. We found most studies were exploring the use of 3D shape prediction as a tool for surgical planning and visualizing the aesthetic outcome.…”

Section: Resultsmentioning

confidence: 99%

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Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review

et al. 2022

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Background Predicting morphological changes to anatomical structures from 3D shapes such as blood vessels or appearance of the face is a growing interest to clinicians. Machine learning (ML) has had great success driving predictions in 2D, however, methods suitable for 3D shapes are unclear and the use cases unknown. Objective and methods This systematic review aims to identify the clinical implementation of 3D shape prediction and ML workflows. Ovid-MEDLINE, Embase, Scopus and Web of Science were searched until 28th March 2022. Results 13,754 articles were identified, with 12 studies meeting final inclusion criteria. These studies involved prediction of the face, head, aorta, forearm, and breast, with most aiming to visualize shape changes after surgical interventions. ML algorithms identified were regressions (67%), artificial neural networks (25%), and principal component analysis (8%). Meta-analysis was not feasible due to the heterogeneity of the outcomes. Conclusion 3D shape prediction is a nascent but growing area of research in medicine. This review revealed the feasibility of predicting 3D shapes using ML clinically, which could play an important role for clinician-patient visualization and communication. However, all studies were early phase and there were inconsistent language and reporting. Future work could develop guidelines for publication and promote open sharing of source code.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review

et al. 2022

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“…Moreover, statistical shape modeling (SSM) has been widely studied in computational anatomy domain for automatic segmentation of biological organs like liver or lumbar spine. This approach was also used to describe the relationship between face and skull in our previous study [8]. However, small number of subjects was commonly used for statistical learning and inference.…”

Section: Discussionmentioning

confidence: 99%