Infrared dermal thermography on diabetic feet soles to predict ulcerations: a case study

Liu, Chanjuan; Heijden, Ferdinand van der; Klein, M.E.; Baal, Jeff G. van; Bus, Sicco A.; Netten, Jaap J. van

doi:10.1117/12.2001807

Cited by 44 publications

(34 citation statements)

References 23 publications

(28 reference statements)

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“…Hence, they used this considerable temperature difference on a heat-map to detect the DFU. Liu et al presented a preliminary case study to evaluate the effectiveness of infra-red dermal thermography on diabetic feet soles to identify pre-signs of ulceration [21]. Harding et al [22] performed a study to assess the infra-red imaging for the prevention of secondary osteomyelitis.…”

Section: Introductionmentioning

confidence: 99%

Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices

Goyal¹,

Reeves²,

Rajbhandari³

et al. 2019

IEEE J. Biomed. Health Inform.

151

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Current practice for Diabetic Foot Ulcers (DFU) screening involves detection and localization by podiatrists. Existing automated solutions either focus on segmentation or classification. In this work, we design deep learning methods for real-time DFU localization. To produce a robust deep learning model, we collected an extensive database of 1775 images of DFU. Two medical experts produced the ground truths of this dataset by outlining the region of interest of DFU with an annotator software. Using 5-fold cross-validation, overall, Faster R-CNN with InceptionV2 model using two-tier transfer learning achieved a mean average precision of 91.8%, the speed of 48 ms for inferencing a single image and with a model size of 57.2 MB. To demonstrate the robustness and practicality of our solution to real-time prediction, we evaluated the performance of the models on a NVIDIA Jetson TX2 and a smartphone app. This work demonstrates the capability of deep learning in real-time localization of DFU, which can be further improved with a more extensive dataset.

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Section: Introductionmentioning

confidence: 99%

Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices

Goyal¹,

Reeves²,

Rajbhandari³

et al. 2019

IEEE J. Biomed. Health Inform.

151

View full text Add to dashboard Cite

show abstract

“…Kaabouch et al [16] evaluated five groups of auto-thresholding techniques, namely histogram shaped-based methods, clustering-based methods, entropy-based methods, object attribute-based methods, and complex genetic algorithms, and concluded that the genetic algorithms based on the thresholding technique give the best results. In 2013, Liu et al [30] implemented the methods described in [16], but the results obtained were not satisfactory in image since there was no clear intensity difference between the foot and the background. They implemented an Active Contours Without Edges (ACWE) method; however, the images without high contrast and visible parts of the ankles and legs still needed manual adjustment.…”

Section: Segmentation and Feature Extractionmentioning

confidence: 99%

Deep Learning Classification for Diabetic Foot Thermograms

Cruz-Vega

Hernandez-Contreras

Peregrina-Barreto

et al. 2020

Sensors

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According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.

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“…In these cases, noncorresponding areas were compared. 17 For instance, in the case in Fig. The method was correspondingly validated: diabetic feet with strong deformations were not included.…”

Section: Asymmetric Analysismentioning

confidence: 99%

Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis

et al. 2015

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Early identification of diabetic foot complications and their precursors is essential in preventing their devastating consequences, such as foot infection and amputation. Frequent, automatic risk assessment by an intelligent telemedicine system might be feasible and cost effective. Infrared thermography is a promising modality for such a system. The temperature differences between corresponding areas on contralateral feet are the clinically significant parameters. This asymmetric analysis is hindered by (1) foot segmentation errors, especially when the foot temperature and the ambient temperature are comparable, and by (2) different shapes and sizes between contralateral feet due to deformities or minor amputations. To circumvent the first problem, we used a color image and a thermal image acquired synchronously. Foot regions, detected in the color image, were rigidly registered to the thermal image. This resulted in 97.8% ± 1.1% sensitivity and 98.4% ± 0.5% specificity over 76 high-risk diabetic patients with manual annotation as a reference. Nonrigid landmark-based registration with B-splines solved the second problem. Corresponding points in the two feet could be found regardless of the shapes and sizes of the feet. With that, the temperature difference of the left and right feet could be obtained.

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Infrared dermal thermography on diabetic feet soles to predict ulcerations: a case study

Cited by 44 publications

References 23 publications

Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices

Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices

Deep Learning Classification for Diabetic Foot Thermograms

Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis

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