Biomedical Engineering

Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.

1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Biomechanical and device modelling

   Finite element models of bones, joints, implants and soft tissue predict stress, strain and motion. Material properties are variable and often uncertain, and boundary conditions from muscle and joint forces are difficult to obtain. Validation against cadaveric or phantom experiments is expected, and sensitivity to material assumptions is reported. Computational fluid dynamics of blood flow requires careful treatment of vessel compliance, non-Newtonian effects and patient-specific geometry.

2. Biosignal processing and feature analysis

   Physiological signals are noisy and affected by artefacts, such as movement or powerline interference. Pipelines include filtering, artefact handling, segmentation and feature extraction. Feature selection and classification must avoid leakage between training and test data, particularly when several samples come from the same subject. Subject-wise cross-validation is the minimum for credible performance estimates.

3. Statistical analysis of experimental and clinical-type datasets

   Studies with human participants raise issues of sample size, repeated measures, multiple comparisons and missing data. Mixed models handle repeated measures; effect sizes and confidence intervals complement p-values. For diagnostic or classification studies, sensitivity, specificity, predictive values and calibration are reported with intervals. Reporting guidelines such as STARD or TRIPOD structure the presentation of such studies.

4. Image-analysis methods

   Segmentation, registration and quantification of medical and biological images use classical and deep-learning methods. Evaluation uses overlap and distance metrics against expert annotations, with attention to inter-observer variability. Datasets must be split by patient, and performance should be examined across scanners and populations. Generalisation claims need external validation.

5. Ethics, data governance and approvals

   Research with human data or participants requires ethical approval, informed consent and data protection compliance. Data should be de-identified, stored securely and used only within the approved scope. This service works only with data for which the researcher has the right and approval to share, and it does not handle identifiable patient data.

6. Translational boundaries and regulation

   Medical devices and software are regulated, and moving from research to clinical use requires verification, validation, risk management and regulatory approval. Research publications should avoid language implying that a device or algorithm is ready for clinical use. We help phrase claims appropriately and describe the next steps required for translation.

7. Verification and validation of computational models

   Credible computational biomedical models document their intended use, verify the numerical solution, validate against relevant experimental data and quantify uncertainty. Guidance documents for computational modelling of medical devices describe credibility evidence proportionate to the model's influence on decisions. Even for pure research, adopting a similar structure improves the quality of the work and helps readers understand how far the model can be trusted.

8. Reporting guidelines and transparency

   Using established reporting guidelines for diagnostic, prognostic or machine-learning studies makes reporting systematic, covering participants, data handling, model development, validation and limitations. Sharing code and de-identified data where permitted supports reproducibility. Negative or inconclusive results are reported in the same detail as positive ones, since they protect the field from over-optimistic expectations.

Common pitfalls this service helps avoid

• Splitting samples rather than subjects. Data from the same person in training and test sets inflates accuracy.
• Implying clinical validity from technical performance. Clinical utility needs appropriately designed studies.
• Using unvalidated material properties for tissues. Results can vary widely with assumed properties.
• Ignoring inter-observer variability. Algorithm performance cannot exceed the reliability of the reference.

What good reporting looks like

A biomedical engineering report describes the device, tissue or signal source, ethics and data governance, the models and validation, the signal or image pipeline with parameters, the statistical analyses and effect sizes with intervals, the external validation if any, and limitations. It is not medical advice and makes no claims of clinical safety, efficacy or regulatory approval.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Imagine a researcher developing an algorithm to detect arrhythmia from ECG recordings. Random segment-wise validation yields 98 percent accuracy, but subject-wise validation gives 88 percent, with lower sensitivity for rare rhythms. The report presents the subject-wise results with confidence intervals, discusses class imbalance and states that the algorithm has not been evaluated in a clinical setting and must not be used for diagnosis.

Questions to settle before you begin

• Is my validation split by subject?
• What ethical approvals govern my data?
• How was my reference standard established?
• What tissue properties are assumed and how uncertain are they?
• What would be needed before any clinical use?

Key terms

Finite element model

A numerical model dividing a structure into elements.

Sensitivity and specificity

Rates of correctly identifying positive and negative cases.

Segmentation

Delineating structures in an image.

Subject-wise cross-validation

Validation in which each person's data appear only in one fold.

Phantom

A physical test object mimicking tissue properties.

Working together on Specialised Applied Sciences projects

Specialised applied-science engagements start with the field-specific question, data and methods, and with an honest check of whether the necessary domain expertise is available for your topic. We ask for protocols, datasets, instrument descriptions and relevant literature. Where a request is highly specialised, feasibility is confirmed before a proposal is issued. Analyses are documented and reproducible, results are reported with uncertainty, and any interpretation beyond the data is labelled. The service does not provide clinical, regulatory or safety advice.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Signal Processing & Sensor Analytics

  Processing and interpretation of sensor and instrumentation signals: filtering, spectral and time-frequency analysis and feature extraction for condition assessment and experiments.

• Computer Vision for Engineering

  Image-based analysis for engineering tasks such as defect detection, microstructure or crack segmentation and dimensional measurement, with careful attention to image acquisition and labelling.

• Finite Element Analysis (FEA)

  Finite element modelling of structures and components for stress, deformation, thermal and dynamic behaviour, with attention to meshing, element choice, boundary conditions and verification against hand calculations or tests.

• Engineering Statistical Analysis

  Statistical analysis of experimental, simulation and field data, with method selection justified by the data and the research question, and results reported with effect sizes and uncertainty rather than p-values alone.