Towards in silico prognosis using big data
Clinical diagnosis and prognosis usually rely on few or even single measurements despite clinical big data being available. This limits the exploration of complex diseases such as adolescent idiopathic scoliosis (AIS) where the associated low bone mass remains unexplained. Observed low physical acti...
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Online Access: | https://doi.org/10.1515/cdbme-2016-0016 |
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doaj-65de8581e324450288bef221199a26c92021-09-06T19:19:23ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042016-09-0121576010.1515/cdbme-2016-0016cdbme-2016-0016Towards in silico prognosis using big dataOhs Nicholas0Keller Fabian1Blank Ole2Lee Yuk-Wai Wayne3Cheng Chun-Yiu Jack4Arbenz Peter5Müller Ralph6Christen Patrik7ETH Zurich, Institute for Biomechanics, Leopold-Ruzicka-Weg 4, 8093 Zurich, SwitzerlandETH Zurich, Institute for Biomechanics, Leopold-Ruzicka-Weg 4, 8093 Zurich, SwitzerlandETH Zurich, Institute for Biomechanics, Leopold-Ruzicka-Weg 4, 8093 Zurich, SwitzerlandDepartment of Orthopaedics & Traumatology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, NT, Hong Kong SAR, ChinaDepartment of Orthopaedics & Traumatology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, NT, Hong Kong SAR, ChinaETH Zurich, Computer Science Department, Universitätstrasse 6, 8092 Zurich, SwitzerlandETH Zurich, Institute for Biomechanics, Leopold-Ruzicka-Weg 4, 8093 Zurich, SwitzerlandETH Zurich, Institute for Biomechanics, Leopold-Ruzicka-Weg 4, 8093 Zurich, SwitzerlandClinical diagnosis and prognosis usually rely on few or even single measurements despite clinical big data being available. This limits the exploration of complex diseases such as adolescent idiopathic scoliosis (AIS) where the associated low bone mass remains unexplained. Observed low physical activity and increased RANKL/OPG, however, both indicate a mechanobiological cause. To deepen disease understanding, we propose an in silico prognosis approach using clinical big data, i.e. medical images, serum markers, questionnaires and live style data from mobile monitoring devices and explore the role of inadequate physical activity in a first AIS prototype. It employs a cellular automaton (CA) to represent the medical image, micro-finite element analysis to calculate loading, and a Boolean network to integrate the other biomarkers. Medical images of the distal tibia, physical activity scores, and vitamin D and PTH levels were integrated as measured clinically while the time development of bone density and RANKL/OPG was observed. Simulation of an AIS patient with normal physical activity and patient-specific vitamin D and PTH levels showed minor changes in bone density whereas the simulation of the same AIS patient but with reduced physical activity led to low density. Both showed unchanged RANKL/OPG and considerable cortical resorption. We conclude that our integrative in silico approach allows to account for a variety of clinical big data to study complex diseases.https://doi.org/10.1515/cdbme-2016-0016adolescent idiopathic scoliosisboolean networkcellular automatonclinical big datamicro-finite element analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ohs Nicholas Keller Fabian Blank Ole Lee Yuk-Wai Wayne Cheng Chun-Yiu Jack Arbenz Peter Müller Ralph Christen Patrik |
spellingShingle |
Ohs Nicholas Keller Fabian Blank Ole Lee Yuk-Wai Wayne Cheng Chun-Yiu Jack Arbenz Peter Müller Ralph Christen Patrik Towards in silico prognosis using big data Current Directions in Biomedical Engineering adolescent idiopathic scoliosis boolean network cellular automaton clinical big data micro-finite element analysis |
author_facet |
Ohs Nicholas Keller Fabian Blank Ole Lee Yuk-Wai Wayne Cheng Chun-Yiu Jack Arbenz Peter Müller Ralph Christen Patrik |
author_sort |
Ohs Nicholas |
title |
Towards in silico prognosis using big data |
title_short |
Towards in silico prognosis using big data |
title_full |
Towards in silico prognosis using big data |
title_fullStr |
Towards in silico prognosis using big data |
title_full_unstemmed |
Towards in silico prognosis using big data |
title_sort |
towards in silico prognosis using big data |
publisher |
De Gruyter |
series |
Current Directions in Biomedical Engineering |
issn |
2364-5504 |
publishDate |
2016-09-01 |
description |
Clinical diagnosis and prognosis usually rely on few or even single measurements despite clinical big data being available. This limits the exploration of complex diseases such as adolescent idiopathic scoliosis (AIS) where the associated low bone mass remains unexplained. Observed low physical activity and increased RANKL/OPG, however, both indicate a mechanobiological cause. To deepen disease understanding, we propose an in silico prognosis approach using clinical big data, i.e. medical images, serum markers, questionnaires and live style data from mobile monitoring devices and explore the role of inadequate physical activity in a first AIS prototype. It employs a cellular automaton (CA) to represent the medical image, micro-finite element analysis to calculate loading, and a Boolean network to integrate the other biomarkers. Medical images of the distal tibia, physical activity scores, and vitamin D and PTH levels were integrated as measured clinically while the time development of bone density and RANKL/OPG was observed. Simulation of an AIS patient with normal physical activity and patient-specific vitamin D and PTH levels showed minor changes in bone density whereas the simulation of the same AIS patient but with reduced physical activity led to low density. Both showed unchanged RANKL/OPG and considerable cortical resorption. We conclude that our integrative in silico approach allows to account for a variety of clinical big data to study complex diseases. |
topic |
adolescent idiopathic scoliosis boolean network cellular automaton clinical big data micro-finite element analysis |
url |
https://doi.org/10.1515/cdbme-2016-0016 |
work_keys_str_mv |
AT ohsnicholas towardsinsilicoprognosisusingbigdata AT kellerfabian towardsinsilicoprognosisusingbigdata AT blankole towardsinsilicoprognosisusingbigdata AT leeyukwaiwayne towardsinsilicoprognosisusingbigdata AT chengchunyiujack towardsinsilicoprognosisusingbigdata AT arbenzpeter towardsinsilicoprognosisusingbigdata AT mullerralph towardsinsilicoprognosisusingbigdata AT christenpatrik towardsinsilicoprognosisusingbigdata |
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