Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.
Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Public Library of Science (PLoS)
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/b68efe3111fc440fac703e9e3903abb9 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:b68efe3111fc440fac703e9e3903abb9 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:b68efe3111fc440fac703e9e3903abb92021-12-02T20:24:10ZMobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.1935-27271935-273510.1371/journal.pntd.0009677https://doaj.org/article/b68efe3111fc440fac703e9e3903abb92021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pntd.0009677https://doaj.org/toc/1935-2727https://doaj.org/toc/1935-2735Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic 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 STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI models.Elena DacalDavid Bermejo-PeláezLin LinElisa ÁlamoDaniel CuadradoÁlvaro MartínezAdriana MousaMaría PostigoAlicia SotoEndre SukosdAlexander VladimirovCharles MwandawiroPaul GichukiNana Aba WilliamsJosé MuñozStella KephaMiguel Luengo-OrozPublic Library of Science (PLoS)articleArctic medicine. Tropical medicineRC955-962Public aspects of medicineRA1-1270ENPLoS Neglected Tropical Diseases, Vol 15, Iss 9, p e0009677 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
| spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Elena Dacal David Bermejo-Peláez Lin Lin Elisa Álamo Daniel Cuadrado Álvaro Martínez Adriana Mousa María Postigo Alicia Soto Endre Sukosd Alexander Vladimirov Charles Mwandawiro Paul Gichuki Nana Aba Williams José Muñoz Stella Kepha Miguel Luengo-Oroz Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
| description |
Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic 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 STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI models. |
| format |
article |
| author |
Elena Dacal David Bermejo-Peláez Lin Lin Elisa Álamo Daniel Cuadrado Álvaro Martínez Adriana Mousa María Postigo Alicia Soto Endre Sukosd Alexander Vladimirov Charles Mwandawiro Paul Gichuki Nana Aba Williams José Muñoz Stella Kepha Miguel Luengo-Oroz |
| author_facet |
Elena Dacal David Bermejo-Peláez Lin Lin Elisa Álamo Daniel Cuadrado Álvaro Martínez Adriana Mousa María Postigo Alicia Soto Endre Sukosd Alexander Vladimirov Charles Mwandawiro Paul Gichuki Nana Aba Williams José Muñoz Stella Kepha Miguel Luengo-Oroz |
| author_sort |
Elena Dacal |
| title |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
| title_short |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
| title_full |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
| title_fullStr |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
| title_full_unstemmed |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
| title_sort |
mobile microscopy and telemedicine platform assisted by deep learning for the quantification of trichuris trichiura infection. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/b68efe3111fc440fac703e9e3903abb9 |
| work_keys_str_mv |
AT elenadacal mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT davidbermejopelaez mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT linlin mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT elisaalamo mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT danielcuadrado mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT alvaromartinez mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT adrianamousa mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT mariapostigo mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT aliciasoto mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT endresukosd mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT alexandervladimirov mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT charlesmwandawiro mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT paulgichuki mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT nanaabawilliams mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT josemunoz mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT stellakepha mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection AT miguelluengooroz mobilemicroscopyandtelemedicineplatformassistedbydeeplearningforthequantificationoftrichuristrichiurainfection |
| _version_ |
1718374071223189504 |