Use of Convolutional Neural Nets and Transfer Learning for Prediction of Surgical Site Infection from Color Images

Fletcher, Roger; Schneider, Gabriel; Hedt‐Gauthier, Bethany; Nkurunziza, Theoneste; Alayande, Barnabas; Riviello, Robert; Kateera, Fredrick

doi:10.1109/embc46164.2021.9630430

Cited by 6 publications

(28 citation statements)

References 10 publications

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“…This phone was chosen for its ability to take high-quality photographs in low light and for its relatively low cost. The Wound Screener application was developed specifically for this study, 34 with the goal of standardising image capture and quality.…”

Section: Methodsmentioning

confidence: 99%

mHealth-community health worker telemedicine intervention for surgical site infection diagnosis: a prospective study among women delivering via caesarean section in rural Rwanda

Nkurunziza

Williams

Kateera³

et al. 2022

BMJ Glob Health

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BackgroundSurgical site infections (SSIs) cause a significant global public health burden in low and middle-income countries. Most SSIs develop after patient discharge and may go undetected. We assessed the feasibility and diagnostic accuracy of an mHealth-community health worker (CHW) home-based telemedicine intervention to diagnose SSIs in women who delivered via caesarean section in rural Rwanda.MethodsThis prospective cohort study included women who underwent a caesarean section at Kirehe District Hospital between September 2019 and March 2020. At postoperative day 10 (±3 days), a trained CHW visited the woman at home, provided wound care and transmitted a photo of the wound to a remote general practitioner (GP) via WhatsApp. The GP reviewed the photo and made an SSI diagnosis. The next day, the woman returned to the hospital for physical examination by an independent GP, whose SSI diagnosis was considered the gold standard for our analysis. We describe the intervention process indicators and report the sensitivity and specificity of the telemedicine-based diagnosis.ResultsOf 787 women included in the study, 91.4% (n=719) were located at their home by the CHW and all of them (n=719, 100%) accepted the intervention. The full intervention was completed, including receipt of GP telemedicine diagnosis within 1 hour, for 79.0% (n=623). The GPs diagnosed 30 SSIs (4.2%) through telemedicine and 38 SSIs (5.4%) through physical examination. The telemedicine sensitivity was 36.8% and specificity was 97.6%. The negative predictive value was 96.4%.ConclusionsImplementation of an mHealth-CHW home-based intervention in rural Rwanda and similar settings is feasible. Patients’ acceptance of the intervention was key to its success. The telemedicine-based SSI diagnosis had a high negative predictive value but a low sensitivity. Further studies must explore strategies to improve accuracy, such as accompanying wound images with clinical data or developing algorithms using machine learning.

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Section: Methodsmentioning

confidence: 99%

mHealth-community health worker telemedicine intervention for surgical site infection diagnosis: a prospective study among women delivering via caesarean section in rural Rwanda

Nkurunziza

Williams

Kateera³

et al. 2022

BMJ Glob Health

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“…Despite the year of publication, we also observed the last year in which the study was conducted. We observed that most of the studies were conducted in 2019 (n = 7, [10,18,20,31,37,41,57]), followed by 2017 (n = 6, [14,22,23,33,44,55,56]), 2018 (n = 6, [17,28,32,34,42,43]), and 2020 (n = 6, [11,15,21,26,27,48]). Only a few studies were conducted in earlier and later years (2021 = 4 [18,24,40,46]; 2022 = 3 [29,30,39]; 2016 = 1 [16]; 2015 = 3 [35,36,38]; 2014 = 1 [19]; 2013 = 2 [12,49]; 2012 = 1 [45]; 2010 = 1 [13]; Unknown = 1 [47]).…”

Section: Description Of Studiesmentioning

confidence: 96%

“…For the 43 studies included in the analysis [55][56][57], most (n = 16) were published in the last year [11,15,17,18,20,21,26,[28][29][30]32,39,40,46,48,57], four in 2022 [24,31,34,42], six in 2021 [10,27,33,35,37,49], seven in 2020 [12,14,25,36,38,43,44], six in 2019 [13,22,23,41,55,56], and four in 2018 [16,19,45,47]. Despite the year of publication, we also observed the last year in which the study was conducted.…”

Section: Description Of Studiesmentioning

confidence: 99%

Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Arzilli,

De Vita,

Pasquale

et al. 2024

Antibiotics

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Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

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“…We have demonstrated that home-based follow-up by CHWs is feasible and acceptable [5,6], but our attempts for simple clinical screenings or sending pictures to general practitioners has resulted in low SSI diagnostic accuracy [5][6][7][8]. In contrast, our machine learning image-based diagnostic algorithms are yielding higher accuracy: 81.3% sensitivity and 65.3% specificity for visible image algorithms [9], increasing to 97% sensitivity and 87% specificity when using computer vision techniques and color calibration in the image processing pipeline, and 95% specificity and 84% specificity for thermal image algorithms [10]. We believe these algorithms hold huge potential for remote SSI diagnostics by health workers in the community in lowand middle-income countries (LMICs), and here we offer some lessons learned to improve others' efforts as they work towards this goal.…”

mentioning

confidence: 89%

“…This target also includes a color pattern that enables automatic image color calibration; this is particularly important if the ambient lighting is not standardized. The use of color calibration along with computer vision methods improved quality of our image training data and considerably improved the model performance, resulting in an increase in area under the curve (AUC) accuracy from 0.64 to 0.86 (9). Using applications to standardize image capture, such as the WoundScreener App, leads to improved color and size consistency across the images that will facilitate algorithm development and deployment [11], and, in our case, eliminated the need for manual cropping (9).…”

mentioning

confidence: 99%

Image-based surgical site infection algorithms to support home-based post-cesarean monitoring: Lessons from Rwanda

Alayande

Prasad²,

Abimpaye³

et al. 2023

PLOS Glob Public Health

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Use of Convolutional Neural Nets and Transfer Learning for Prediction of Surgical Site Infection from Color Images

Cited by 6 publications

References 10 publications

mHealth-community health worker telemedicine intervention for surgical site infection diagnosis: a prospective study among women delivering via caesarean section in rural Rwanda

mHealth-community health worker telemedicine intervention for surgical site infection diagnosis: a prospective study among women delivering via caesarean section in rural Rwanda

Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Image-based surgical site infection algorithms to support home-based post-cesarean monitoring: Lessons from Rwanda

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