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Global Health Technologies

Undergraduate Lightning Talks

Automated Depth of Field Analysis to Characterize a Low-Cost Laparoscope Designed for Low- and Middle-Income Countries

Saturday, October 26, 2024
1:15 PM – 1:20 PM EST
Location: Room 342

Introduction:  Minimally invasive laparoscopic surgery is an attractive alternative to conventional open surgery as it is known to improve patient outcomes, cause less scarring, and lead to significantly faster patient recovery1,2. However, low- and middle-income countries (LMICs) often lack access to laparoscopic surgery due to high installation costs (over $200,000) and fragile equipment, leading to ongoing maintenance costs3. To address this, we are developing the KeyScope laparoscope, a low-cost, durable alternative designed to make laparoscopic surgery more accessible in LMICs. For the KeyScope to be reliably manufactured, it must meet requirements, such as depth of field (DOF) thresholds - the distance over which the camera can maintain focus. The manual calculation of this parameter is time-consuming and leads to data variability. To eliminate this issue, we developed a semi-automated algorithm in MATLAB to increase efficiency and consistency.

Materials and

Methods:  We captured a series of images of the DOF target (Edmund Optics, 54-440) with our latest version of the KeyScope. To manually calculate the DOF, images were analyzed in ImageJ by drawing a line along the horizontal lines on the right-hand side of the target, which has 5 line pairs per millimeter (Figure 1A). Using the 'plot profile' function in ImageJ, an intensity plot was generated (Figure 1B). The difference between the first full peak (white pixels) and trough (black pixels) was measured. Then, the failure point was determined when the difference between a peak and subsequent trough fell below half of the initial value4. The number of peaks between the start and failure points was divided by 5 to obtain the DOF in millimeters. This process was replicated in our MATLAB code. Our code requests the user to draw the line on the target image, and all subsequent calculations are performed automatically. Five different users analyzed the same set of KeyScope images with both the manual method and semi-automated MATLAB code.

Results, Conclusions, and Discussions: The semi-automated code improved efficiency and consistency in DOF calculations compared to the manual method (Figure 2). There was a significant decrease in the time required to obtain the DOF measurements when users used the manual analysis (4.8 minutes) compared to the MATLAB code (20 seconds, p< 0.0001). There was also less inter-user variability when the users ran the MATLAB code (0.21) compared to the manual analysis (1.5). Because the algorithm runs on standardized steps, any possible error in manually counting peaks is eliminated. In the future, our code will be moved to open-source Python and shared with our collaborators in Uganda who will manufacture the KeyScope.

Acknowledgements (Optional):  

References: 1Garry R et al, Best Pract Res Clin Obstet Gynaecol. 2006., 2Liu X et al, Int J Computer Assist Radiol Surg. 2016, 3Mueller, J. Ann Biomed Eng 49, 2021, 4Zadey S et al, JAMA Surg. 2022