## 4 Discussion  
This study provides a complete description of TM mechanics, including the 3D shape information and full-field TM surface normal vibration in both the time and frequency domains.  
The shape results show that the TM shape is affected by various middle-ear manipulations. The different manipulations overall introduce a maximum shape change of less than 0.3 mm. The fluid injection pushes the TM to deform towards the ear canal side, while the IS joint interruption seems to pull the TM toward the middle-ear cavity side, which is interesting and may suggest the cochlear load produced tension on the TM. No differentiable shape change is identified for open cavity and stages immobilization.  
Figure 11: The complex model indicator function of TB3–5 under different conditions.  
Figure 12: The complex model indicator function subtracted by the closed cavity case of TB3–5.  
Displacements in the time domain show we can confidently distinguish half fluid injection and full fluid injection by checking the RMS results and the open cavity case. Fluid injections will surpass the TM motion. Opening the cavity causes the TM surface to displace more compared to the normal middle-ear cavity condition (closed cavity).  
In the frequency domain, we observed trends in different CMIFs associated with different middle-ear conditions. In open cavity cases, the first significant CMIF peak appears around 4 kHz (3.7 kHz for TB3, 3.5 kHz for TB4, and 4.3 kHz for TB5). Half fluid and full fluid injections decrease the CMIF across the whole frequency range. Stages immobilization reduces the CMIF at a lower frequency up to 2.5 kHz for TB3 and TB4 and increases it for TB5 at a higher frequency (12-14 kHz). IS joint interruption adds additional peaks to the CMIF at different frequencies.  
Time domain and frequency domain analysis results can separate different middle-ear pathologies, suggesting the potential for clinical diagnosis. In the future work, we will investigate the accuracy of potential diagnostics through further experiments--identify trends in the data associated with different pathologies and test the sensitivity and selectivity of these analyses for clinical diagnosis. Artificial intelligence (AI) and data mining will be applied to automate the analysis process and assist in the separation of normal and diseased states.  
Conceptualization, N.M., J.J.R., C.F., J.T.C.; methodology, H.T., P.R., P.P.; software, H.T., P.R., P.P., N.M.; validation, H.T., P.P.; formal analysis, H.T.; P.P.; investigation, H.T.; resources, J.J.R., C.F., J.T.C.; data curation, H.T., writing--original draft preparation, H.T.; writing--review and editing, H.T., P.R., K.P., P.P., J.J.R., J.T.C.; visualization, H.T.; supervision, J.J.R., C.F., J.T.C.; project administration, J.J.R., C.F., J.T.C.; funding acquisition, J.J.R., C.F., J.T.C.  
This work has been funded by the National Institutes of Health (NIH), National Institute on Dearness and Other Communication Disorders (NIDCD), grant number R01, DC016079.  
This work is supported by the NanoEngineering, Science, and Technology (NEST) program at the Worcester Polytechnic Institute, Mechanical Engineering Department.  
The authors declare no conflict of interest.