Physical Factors Investigation on Surgical Dexterity Parameters Using Computer-based Assessment System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-4
Year of Publication : 2021
Authors : Y.K. Cham, E. L. M. Su, C.F. Yeong, S.N.Z. Ahmmad, S. Sood, A. Gandhi
DOI :  10.14445/22315381/IJETT-V69I4P205


MLA Style: Y.K. Cham, E. L. M. Su, C.F. Yeong, S.N.Z. Ahmmad, S. Sood, A. Gandhi  "Physical Factors Investigation on Surgical Dexterity Parameters Using Computer-based Assessment System" International Journal of Engineering Trends and Technology 69.4(2021):27-33. 

APA Style:Y.K. Cham, E. L. M. Su, C.F. Yeong, S.N.Z. Ahmmad, S. Sood, A. Gandhi. Physical Factors Investigation on Surgical Dexterity Parameters Using Computer-based Assessment System  International Journal of Engineering Trends and Technology, 69(4),27-33.

Surgical dexterity is one of the crucial metrics for evaluating candidates during surgical residency training. Many factors influence surgical dexterity performance, but they are not studied in depth. Hence, the objective of this study is to investigate the correlation between factors and surgical dexterity performance with the aid of a computer-based assessment system. A custom data acquisition module was developed, namely the “Green Target Module,” to acquire positional data of hand movements from the subjects when controlling a cursor in a 3D virtual reality (VR) scene. The positional data were recorded and extracted into seven objective parameters, which are motion path length, the economy of movement, motion smoothness, motion path accuracy, motion path precision, endpoint accuracy, and endpoint precision. Body posture, magnification, and handedness were investigated to figure out a preferable setup for better performance. A total of thirty-four subjects from different surgical backgrounds were recruited for the experiments. Fourteen trials were recorded in each test, and every subject was required to complete eight tests with different experimental configurations. Results showed that endpoint accuracy while sitting was significantly better than standing. Using 10x magnification during surgical dexterity assessment showed significantly better performance outcomes than 1x magnification. Performing dexterity test using dominant hand also showed significantly better when compared to non-dominant hand.

[1] C. Villanueva, M. Doyle, J. Berger, G. Fermanis, and D. Glenn., Surgical Removal of an Unusual Massive Mediastinal Tumour Preceded by Interventional Embolisation, Hear. Lung Circ., 26 (2) (2017) e7–e10. doi: 10.1016/j.hlc.2016.07.013.
[2] R. K. Khouri et al., Tissue-engineered breast reconstruction with brava-assisted fat grafting: A 7-year, 488-patient, multicenter experience, Plast. Reconstr. Surg., 135(3) (2015) 643–658. doi: 10.1097/PRS.0000000000001039.
[3] Z. Li et al., Acute Blockage of Notch Signaling by DAPT Induces Neuroprotection and Neurogenesis in the Neonatal Rat Brain After Stroke, Transl. Stroke Res., 7(2) (2016) 132–140. doi: 10.1007/s12975-015-0441-7.
[4] A. Joseph, S. Bayramzadeh, Z. Zamani, and B. Rostenberg., Safety, Performance, and Satisfaction Outcomes in the Operating Room: A Literature Review, Heal. Environ. Res. Des. J., 11(2) (2018) 137–150. doi: 10.1177/1937586717705107.
[5] L. A. Yurteri-Kaplan et al., Sitting versus standing makes a difference in musculoskeletal discomfort and postural load for surgeons performing vaginal surgery, Int. Urogynecol. J., 30(2) (2018) 231–237. doi: 10.1007/s00192-018-3619-1.
[6] M. L. Carlson, D. J. Archibald, A. J. Sorom, and E. J. Moore., Under the microscope: Assessing surgical aptitude of otolaryngology residency applicants, Laryngoscope, 120(6) (2009) 1109–1113. doi: 10.1002/lary.20914.
[7] N. F. Z. Hisham, A. A. Jamal, and W. M. R. W. Idris., Lower limb walking gait profiling using marker-less motion capture with GDL and R-GDL methods to assist physiotherapy treatment, IJETT Int. J. Eng. Trends Technol.,1 (2020) 44–51. doi: 10.14445/22315381/CATI2P207.
[8] L. Sbernini, L. R. Quitadamo, F. Riillo, N. D. Lorenzo, A. L. Gaspari, and G. Saggio., Sensory-Glove-Based Open Surgery Skill Evaluation, IEEE Trans. Human-Machine Syst., (2018) 1–6. doi: 10.1109/THMS.2017.2776603.
[9] J. Gong and J. Lach., Motion marker discovery from inertial body sensors for enhancing objective assessment of robotic surgical skills, 4th Int. Symp. Bioelectron. Bioinformatics, ISBB,(2015) 215–218. doi: 10.1109/ISBB.2015.7344962.
[10] T. J. Kirby., Dexterity testing and residents’ surgical performance, Trans. Am. Ophthalmol. Soc., 77 (1979) 294–307.
[11] J. Peirs et al., Design of an Optical Force Sensor for Force Feedback during Minimally Invasive Robotic Surgery, Eurosensors XVII, (2003) 1063–1066. [Online].Available:
[12] S.-H. Mi, Z.-G. Hou, F. Yang, X.-L. Xie, and G.-B. Bian., A collision response algorithm for 3D virtual reality minimally invasive surgery simulator, 26th Chinese Control Decis. Conf. (2014 CCDC), (2014) 4594–4599. doi: 10.1109/CCDC.2014.6852993.
[13] V. Datta, S. Mackay, M. Mandalia, and A. Darzi., The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in the laboratory-based model, J. Am. Coll. Surg., 193(5) (2001) 479–485. doi: 10.1016/S1072-7515(01)01041-9.
[14] A. J. Hung, I. S. Jayaratna, K. Teruya, M. M. Desai, I. S. Gill, and A. C. Goh., Comparative assessment of three standardized robotic surgery training methods, BJU Int., 112(6) (2013) 864–871. doi: 10.1111/bju.12045.
[15] J. Bric, M. Connolly, A. Kastenmeier, M. Goldblatt, and J. C. Gould., Proficiency training on a virtual reality robotic surgical skills curriculum, Surg. Endosc., 28(12) (2014) 3343–3348. doi: 10.1007/s00464-014-3624-5.
[16] P. A. Kenney, M. F. Wszolek, J. J. Gould, J. A. Libertino, and A. Moinzadeh., Face, Content, and Construct Validity of dV-Trainer, a Novel Virtual Reality Simulator for Robotic Surgery, Urology, 73(6) (2009) 1288–1292. doi: 10.1016/j.urology.2008.12.044.
[17] V. Quan, B. Alaraimi, W. Elbakbak, A. Bouhelal, and B. Patel., Crossover study of the effect of coffee consumption on simulated laparoscopy skills, Int. J. Surg., 14 (2015) 90–95. doi: 10.1016/j.ijsu.2015.01.004.
[18] P. J. Bongers, P. D. Van Hove, L. P. S. Stassen, J. Dankelman, and H. W. R. Schreuder., A new virtual-reality training module for laparoscopic surgical skills and equipment handling: Can multitasking be trained? A randomized controlled trial, J. Surg. Educ., 72(2) (2015) 184–191.doi: 10.1016/j.jsurg.2014.09.004.
[19] J. Y. Lee, P. Mucksavage, D. C. Kerbl, V. B. Huynh, M. Etafy, and E. M. McDougall., a Validation study of a virtual reality robotic simulator role as an assessment tool? J. Urol., 187(3) (2012) 998–1002. doi: 10.1016/j.juro.2011.10.160.
[20] G. Islam, K. Kahol, B. Li, M. Smith, and V. L. Patel., Affordable, web-based surgical skill training and evaluation tool, J. Biomed. Inform., 59 (2016) 102–114. doi: 10.1016/j.jbi.2015.11.002.
[21] V. R. Ramakrishnan and B. M. Milam., Ergonomic analysis of the surgical position in functional endoscopic sinus surgery, Int. Forum Allergy Rhinol., 7(6) (2017) 570–575. doi: 10.1002/alr.21911.
[22] M. Eichenberger, N. Biner, M. Amato, A. Lussi, and P. Perrin., Effect of Magnification on the Precision of Tooth Preparation in Dentistry, Oper. Dent., 43(5) (2018) 501–507. doi: 10.2341/17-169-c.
[23] A. Skinner, G. Auner, M. Meadors, and M. Sebrechts., Ambidexterity in laparoscopic surgical skills training,” Stud. Health Technol. Inform., 184 (2013) 412–416. doi: 10.3233/978-1-61499-209-7-412.
[24] F. H. F. Elneel, F. Carter, B. Tang, and A. Cuschieri., Extent of innate dexterity and ambidexterity across handedness and gender: Implications for training in laparoscopic surgery, Surg. Endosc. Other Interv. Tech., 22(1) (2008) 31–37. doi: 10.1007/s00464-007-9533-0.
[25] S. N. Z. Ahmmad, E. Su Lee Ming, Y. Che Fai, S. Sood, and A. Gandhi., Objective measurement for surgical skill evaluation, J. Teknol., 78 (2016) 7–5,145–152. doi: 10.11113/jt.v78.9461.
[26] S. N. Z. Ahmmad et al., Objective assessment of surgeon’s psychomotor skill using virtual reality module, Indones. J. Electr. Eng. Comput. Sci., 14(3) (2019) 1533–1543. doi: 10.11591/ijeecs.v14.i3.pp1533-1543.
[27] E. L. M. Su, T. L. Win, W. T. Ang, T. C. Lim, C. L. Teo, and E. Burdet., Micromanipulation accuracy in pointing and tracing investigated with a contact-free measurement system, Proc. 31st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Eng. Futur. Biomed. EMBC, (2009) 3960–3963. doi: 10.1109/IEMBS.2009.5333665.
[28] F. S. Nahm., Nonparametric statistical tests for the continuous data: The basic concept and the practical use, Korean J. Anesthesiol., 69(1) (2016) 8–14. doi: 10.4097/kjae.2016.69.1.8.
[29] L. Mattos, D. Caldwell, G. Peretti, F. Mora, L. Guastini, and R. Cingolani., Microsurgery robots: addressing the needs of high-precision surgical interventions, Swiss Med. Wkly., 146 (2016). doi: 10.4414/smw.2016.14375.
[30] S. S. Srouji et al., Robotic Assistance Confers Ambidexterity to Laparoscopic Surgeons, J. Minim. Invasive Gynecol., 125(1) (2017) 76–83. doi: 10.1016/j.jmig.2017.07.010.

Manual dexterity; assessment parameters; assessment factors; body posture; visual magnification; handedness