Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

Abstract

Anemia is a global public health problem with major consequences for human health. About a quarter of the world population shows a hemoglobin concentration that is below the recommended thresholds. Non-invasive methods for monitoring and identifying potential risk of anemia and smartphone-based devices to perform this task are promising in addressing this pathology. We have considered some well-known studies carried out on this topic since the main purpose of this work was not to produce a review. The first group of papers describes the approaches for the clinical evaluation of anemia focused on different human exposed tissues, while we used a second group to overview some technologies, basic methods, and principles of operation of some devices and highlight some technical problems. Results extracted from the second group of papers examined were aggregated in two comparison tables. A growing interest in this topic is demonstrated by the increasing number of papers published recently. We believe we have identified several critical issues in the published studies, including those published by us. Just as an example, in many papers the dataset used is not described. With this paper we wish to open a discussion on these issues. Few papers have been sufficient to highlight differences in the experimental conditions and this makes the comparison of the results difficult. Differences are also found in the identification of the regions of interest in the tissue, descriptions of the datasets, and other boundary conditions. These critical issues are discussed together with open problems and common mistakes that probably we are making. We propose the definition of a road-map and a common agenda for research on this topic. In this sense, we want to highlight here some issues that seem worthy of common discussion and the subject of synergistic agreements. This paper, and in particular, the discussion could be the starting point for an open debate about the dissemination of our experiments and pave the way for further updates and improvements of what we have outlined.

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

“…To carry out a preliminary and instantaneous diagnosis, the study of anamnesis and the physical examination are often used in clinical practice [14][15][16][17][18][19][20][21][22]. Health professionals perform a physical examination of anatomical sites such as conjunctiva, tongue, palmar crease, nail beds since they are indicators to raise suspicion of anemia, but it must also be said that there are less optimistic findings, for example, Da Silva et al [21] evaluated the correlation between the opinions expressed by different doctors and pointed out that the ideal condition for examining skin color is with natural daylight or without direct light on the skin: Therefore the different ambient lighting can influence a correct diagnosis.…”

Biomedical Image Processing and Classification

“…The segmentation task has been further investigated by a subsequent work, proposing a deep-learning-based approach involving a deconvolutional neural network [17]. The overall pipeline for building a reliable estimator is composed of several smaller tasks having multiple research challenges [18,19]. For instance, starting from the digital image capturing phase, the process is affected by heterogeneous ambient lighting conditions and intrinsic color balancing techniques by the device [20].…”

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

“…Systems able to support the doctors’ work in the diagnosis of pathologies can facilitate health care decision making reducing considerably expenditure of time and money [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. The ECG has become the diagnostic procedure most commonly performed in clinical cardiology [ 13 , 14 , 15 ] and the diffusion of wearable and portable devices has been enabling patients to constantly monitor the cardiac activity, for example of elder people through wireless sensor networks [ 16 ].…”