Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population
Objective Determine whether data collected from a smartphone camera can be used to detect anemia in a pediatric population. Methods HEMO-AI (Hemoglobin Easy Measurement by Optical Artificial Intelligence), a clinical study carried out from December 2020 to February 2023, recruited patients from the...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-12-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241297057 |
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| author | Daniel Gordon Jason Hoffman Keren Gamrasni Yotam Barlev Alex Levine Tamar Landau Ronen Shpiegel Avishai Lahad Ariel Koren Carina Levin Osnat Naor Hannah Lee Xin Liu Shwetak Patel Gilad Chayen Michael Brandwein |
| author_facet | Daniel Gordon Jason Hoffman Keren Gamrasni Yotam Barlev Alex Levine Tamar Landau Ronen Shpiegel Avishai Lahad Ariel Koren Carina Levin Osnat Naor Hannah Lee Xin Liu Shwetak Patel Gilad Chayen Michael Brandwein |
| author_sort | Daniel Gordon |
| collection | DOAJ |
| description | Objective Determine whether data collected from a smartphone camera can be used to detect anemia in a pediatric population. Methods HEMO-AI (Hemoglobin Easy Measurement by Optical Artificial Intelligence), a clinical study carried out from December 2020 to February 2023, recruited patients from the Pediatric Emergency Department, Pediatric Inpatient Department and Pediatric Hematology Unit of the Haemek Medical Center, Afula, Israel. A population-based sample of 823 patients aged 6 months to 18 years who had undergone a venous blood draw for a complete blood count since being admitted to the hospital were enrolled. Patients with total leukonychia, nailbed darkening or discoloration due to medication, nail clubbing, clinically indicated jaundice, subungual hematoma, nailbed lacerations, avulsion injuries, or nail polish applied on fingernails were not eligible for study recruitment. Video and images of the patients’ hand placed in a collection chamber were collected using a smartphone camera. Results 823 samples, 531 from a 12.2 megapixel camera and 256 from a 12.2 megapixel camera, were collected. 26 samples were excluded by the study coordinator for irregularities. 97% of fingernails and 68% of skin samples were successfully identified by a post-trained machine learning model. Separate models built to detect anemia using images taken from the Pixel 3 had an average precision of 0.64 and an average recall of 0.4, whereas models built using the Pixel 6 had an average precision of 0.8 and an average recall of 0.84. Further supplementation of training data with synthetic data boosted the precision of the latter to 0.84 and the average recall to 0.87. Conclusions This study lays the groundwork for the future evolution of non-invasive, pain-free, and accessible anemia screening tools tailored specifically for pediatric patients. It identifies important sample collection parameters and design, provides critical algorithms for the pre-processing of fingernail data, and reports an initial capability to detect anemia with 87% sensitivity and 84% specificity. Trial Registration Prospectively registered on www.clinicaltrials.gov (Identifier: NCT04573244) on 15 September 2020, prior to subject recruitment. |
| format | Article |
| id | doaj-art-939abdf08c0541129a3b225ba2ef1cf5 |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-939abdf08c0541129a3b225ba2ef1cf52025-01-18T05:03:19ZengSAGE PublishingDigital Health2055-20762024-12-011010.1177/20552076241297057Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric populationDaniel Gordon0Jason Hoffman1Keren Gamrasni2Yotam Barlev3Alex Levine4Tamar Landau5Ronen Shpiegel6Avishai Lahad7Ariel Koren8Carina Levin9Osnat Naor10Hannah Lee11Xin Liu12Shwetak Patel13Gilad Chayen14Michael Brandwein15 Department of Research & Development, MyOr Diagnostics Ltd., Zichron Yaakov, Israel Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA Department of Research & Development, MyOr Diagnostics Ltd., Zichron Yaakov, Israel Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Department of Research & Development, MyOr Diagnostics Ltd., Zichron Yaakov, Israel , Afula, Israel , Afula, Israel , Afula, Israel , Afula, Israel , Afula, Israel Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA , Afula, Israel Department of Molecular Biology, Ariel University, Ariel, IsraelObjective Determine whether data collected from a smartphone camera can be used to detect anemia in a pediatric population. Methods HEMO-AI (Hemoglobin Easy Measurement by Optical Artificial Intelligence), a clinical study carried out from December 2020 to February 2023, recruited patients from the Pediatric Emergency Department, Pediatric Inpatient Department and Pediatric Hematology Unit of the Haemek Medical Center, Afula, Israel. A population-based sample of 823 patients aged 6 months to 18 years who had undergone a venous blood draw for a complete blood count since being admitted to the hospital were enrolled. Patients with total leukonychia, nailbed darkening or discoloration due to medication, nail clubbing, clinically indicated jaundice, subungual hematoma, nailbed lacerations, avulsion injuries, or nail polish applied on fingernails were not eligible for study recruitment. Video and images of the patients’ hand placed in a collection chamber were collected using a smartphone camera. Results 823 samples, 531 from a 12.2 megapixel camera and 256 from a 12.2 megapixel camera, were collected. 26 samples were excluded by the study coordinator for irregularities. 97% of fingernails and 68% of skin samples were successfully identified by a post-trained machine learning model. Separate models built to detect anemia using images taken from the Pixel 3 had an average precision of 0.64 and an average recall of 0.4, whereas models built using the Pixel 6 had an average precision of 0.8 and an average recall of 0.84. Further supplementation of training data with synthetic data boosted the precision of the latter to 0.84 and the average recall to 0.87. Conclusions This study lays the groundwork for the future evolution of non-invasive, pain-free, and accessible anemia screening tools tailored specifically for pediatric patients. It identifies important sample collection parameters and design, provides critical algorithms for the pre-processing of fingernail data, and reports an initial capability to detect anemia with 87% sensitivity and 84% specificity. Trial Registration Prospectively registered on www.clinicaltrials.gov (Identifier: NCT04573244) on 15 September 2020, prior to subject recruitment.https://doi.org/10.1177/20552076241297057 |
| spellingShingle | Daniel Gordon Jason Hoffman Keren Gamrasni Yotam Barlev Alex Levine Tamar Landau Ronen Shpiegel Avishai Lahad Ariel Koren Carina Levin Osnat Naor Hannah Lee Xin Liu Shwetak Patel Gilad Chayen Michael Brandwein Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population Digital Health |
| title | Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population |
| title_full | Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population |
| title_fullStr | Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population |
| title_full_unstemmed | Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population |
| title_short | Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population |
| title_sort | artificial intelligence enabled non invasive ubiquitous anemia screening the hemo ai pilot study on pediatric population |
| url | https://doi.org/10.1177/20552076241297057 |
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