Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional

Field-portable reflection and transmission microscopy based on lensless holography.

We demonstrate a lensfree dual-mode holographic microscope that can image specimens in both transmission and reflection geometries using in-line transmission and off-axis reflection holography, respectively. This field-portable dual-mode holographic microscope has a weight of ~200 g with dimensions of 15 x 5.5 x 5cm, where a laser source is powered by two batteries. Based on digital in-line holography, our transmission microscope achieves a sub-pixel lateral resolution of ≤2 µm over a wide field-of-view (FOV) of ~24 mm(2) due to its unit fringe magnification geometry. Despite its simplicity and ease of operation, in-line transmission geometry is not suitable to image dense or connected objects such as tissue slides since the reference beam gets distorted causing severe aberrations in reconstruction of such objects. To mitigate this challenge, on the same cost-effective and field-portable assembly we built a lensless reflection mode microscope based on digital off-axis holography where a beam-splitter is used to interfere a tilted reference wave with the reflected light from the object surface, creating an off-axis hologram of the specimens on a CMOS sensor-chip. As a result of the reduced space-bandwidth product of the off-axis geometry compared to its in-line counterpart, the imaging FOV of our reflection mode is reduced to ~9 mm(2), while still achieving a similar sub-pixel resolution of ≤2 µm. We tested the performance of this compact dual-mode microscopy unit by imaging a US-air force resolution test target, various micro-particles as well as a histopathology slide corresponding to skin tissue. Due to its compact, cost-effective, and lightweight design, this dual-mode lensless holographic microscope might especially be useful for field-use or for conducting microscopic analysis in resource-poor settings

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional

Summary of experimental results for diagnosis of malaria infected red blood cells.

<p>Summary of experimental results for diagnosis of malaria infected red blood cells.</p

The Crowd Effect: gamer performance results for experiment #5.

<p>The plots show the worst case scenarios where the diagnoses from the worst performing players are used to generate an overall diagnosis for each RBC in the game. Note that the specificity (or true negative rate) is always very high for the gamers, and does not improve much as more gamers are added to the mix. However, the sensitivity (or true positive rate) benefits the most as more players are added, and climbs above 95% once 15-gamers form the crowd. The accuracy also increases as more players are added, but since it reflects both the specificity and the sensitivity its increase is not as drastic as that of the sensitivity.</p

The hybrid (human + machine) diagnostics framework.

<p>As new images are generated, they are diagnosed by pre-trained machine learning algorithms. The confidence of these algorithms in their decisions determines whether the images should be passed on to human gamers or not. Once the <i>difficult-to-diagnose</i> images for these algorithms are crowd-sourced and are diagnosed by the human gamers, they are merged with the <i>easy-to-diagnose</i> images to compute the final diagnostics results. The data is then fed back through the system and added to the training dataset used by the machine learning algorithms. During each cycle, self-learning algorithms will improve as a result of added training data. ‘T’ refers to a threshold value.</p

Overview of the gaming analysis framework.

<p>The images are treated as a sequence of binary values that are broadcast by the server. The gamers are effectively noisy repeaters that in the most ideal case output the correct symbol for the inputs that they receive. Each repeater transmits its own noisy version of the same input symbol to a decoder. The decoder combines all the received repeater outputs and decodes a final output z_i, which ideally will be the correct label/diagnosis for the input images. The repeaters can be modelled as Binary Communication Channels (top-left). <i>p_ij</i> corresponds to the probability of receiving symbol <i>j</i> when in fact symbol <i>i</i> was transmitted.</p

Proposed platform.

<p><b>A</b>) Biomedical data (e.g., images of thin blood smear samples) from individual light microscopes all around the world are transmitted to data centres where they are pre-processed and digitally distributed among gamers, which in turn diagnose and transmit their responses back. These individual results of the gamers are then fused toward a final diagnosis, the result of which is transmitted back to the point-of-care or the clinic/hospital. In the map above, orange-coloured regions show locations where risk of contraction of malaria still exists. <b>B</b>) Block diagram of the presented platform.</p