Artificial intelligence, diagnostic imaging and neglected tropical diseases: ethical implications

Vaisman, A; Linder, Nina; Lundin, Johan; Orchanian‐Cheff, Ani; Coulibaly, Jean T.; Ephraim, Richard K. D.; Bogoch, Isaac I.

doi:10.2471/blt.19.237560

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…However, AI systems have also the potential to be applied to enable a rapid and objective diagnosis of NTDs and to enable public health delivery in low- and middle-income countries. In this context an special effort has to be made for the application of AI methods in such diseases as has been recommended by the World Health Organization (WHO) (13).…”

Section: Introductionmentioning

confidence: 99%

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Dacal¹,

Bermejo-Peláez²,

Lin

et al. 2021

Preprint

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Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). Kato-Katz technique is the diagnosis method recommended by WHO and although is generally more sensitive than other microscopic methods in high transmission settings, it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence methods based on digitized samples can support diagnostics efforts by support diagnostics efforts by performing an automatic and objective quantification of disease infection.In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of soil-transmitted helminths. Our solution includes (1) a digitalization system based on a mobile app that digitizes the microscope samples using a low-cost 3D-printed microscope adapter, (2) a telemedicine platform for remote analysis and labelling and (3) novel deep learning algorithms for automatic assessment and quantification of parasitological infection of STH.This work has been evaluated by comparing the STH quantification using both a manual remote analysis based on the digitized images and the AI-assisted quantification against the reference method based on conventional microscopy. The deep learning algorithm has been trained and tested on 41 slides of stool samples containing 949 eggs from 6 different subjects using a cross-validation strategy obtaining a mean precision of 98,44% and mean recall of 80,94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs.In conclusion, this work has presented a comprehensive pipeline using smartphone-based microscopy integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using artificial intelligence models.

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Section: Introductionmentioning

confidence: 99%

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Dacal¹,

Bermejo-Peláez²,

Lin

et al. 2021

Preprint

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“…Different terms are used in connection with AI, such as machine learning, deep learning, and conventional neural networks [ 1 ]. Furthermore, there is no universally agreed-upon definition of AI, while it is suggested to define it as a system capable of interpreting and learning from data to produce a specific goal [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Value assessment of artificial intelligence in medical imaging: a scoping review

et al. 2022

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Background Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. Methods We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. Results Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. Conclusions This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.

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“…As pointed out by the WHO, AI has great potential for neglected tropical infections such as MPXV, but ethical and privacy considerations for AI tools have to be carefully taken into account, such as where user data are stored and data stewardship 29 . As with any infectious disease, and as is the case with MPXV, recognizing early symptoms to guide the patient toward a timely diagnosis is critical, potentially preventing severe disease, complications and secondary infections 30 .…”

Section: Discussionmentioning

confidence: 99%

A deep-learning algorithm to classify skin lesions from mpox virus infection

Thieme

Zheng

Machiraju

et al. 2023

Nat Med

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Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.

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Artificial intelligence, diagnostic imaging and neglected tropical diseases: ethical implications

Cited by 10 publications

References 11 publications

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Value assessment of artificial intelligence in medical imaging: a scoping review

A deep-learning algorithm to classify skin lesions from mpox virus infection

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