Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria

Srivastava, Bina; Anvikar, Anupkumar R.; Ghosh, Susanta Kumar; Mishra, Neelima; Kumar, Navin; Houri-Yafin, Arnon; Pollak, Joseph Joel; Salpeter, Seth J.; Valecha, Neena

doi:10.1186/s12936-015-1060-1

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…In a previous study [22] we presented the first clinically available computer vision based reader for malaria diagnostics. The P1 device showed a sensitivity of 97.05%, specificity of 96.33% and speciation of 73.3% Plasmodium falciparum and 91.4% for Plasmodium vivax.…”

Section: Discussionmentioning

confidence: 99%

“…Previous attempts at a creating a computer microscopist have not surpassed the development stage [20,21]. A recent report from our group, described a clinically available automated microscopist which was tested with the National Institute of Malaria Research India [22]. The device, the SightDx P1 malaria platform, was tested on 431 patients and demonstrated a sensitivity of 97.05%, and a specificity of 96.33% when compared with PCR.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Computer Vision Platform for Clinical Diagnosis of Malaria

Houri-Yafin¹,

Eshel²

2015

Malaria Contr Elimination

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Accurate malaria diagnosis is necessary to prevent unnecessary deaths and curb malaria drug resistance related to unnecessary treatment. While numerous diagnostic assays exist, the need for a low-cost, rapid and highly accurate malaria test remains. Here we evaluate the diagnostic performance of a computer vision platform, the Sight Diagnostic P2 device for malaria diagnosis, speciation and parasite quantification. The trial was conducted at two centers on Plasmodium falciparum and Plasmodium vivax samples, using different testing protocols: 374 samples were collected at City Hospital Mangalore India and 167 samples were collected at Lancet Laboratories Johannesburg South Africa. At City Hospital, the device diagnoses were compared to RT-PCR results while at Lancet Laboratories the device diagnoses were compared to a panel of tests provided by the clinic. For identification of malaria, the device demonstrated a sensitivity of 97% and a specificity of 99.5% at City Hospital India, and a sensitivity of 97.8% and a specificity of 97.5% at Lancet Laboratories Johannesburg. For speciation, the device correctly identified 87.5% for Plasmodium Vivax and 93.5% for Plasmodium Falciparum at City Hospital India. Lastly, comparing the device parasite count with that of trained microscopes, produced an average pearsons correlation of 0.87.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Computer Vision Platform for Clinical Diagnosis of Malaria

Houri-Yafin¹,

Eshel²

2015

Malaria Contr Elimination

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“…Microscopy Mixed 12 0 0 0 12 Negative 0 42 3 5 50 pf 0 0 10 0 10 pv 0 0 0 28 28 Total 12 42 13 33 0 0 6 Negative 3 42 5 12 62 pf 1 0 8 0 9 pv 2 0 0 21 23 Total 12 42 13 33 100 The Parasight digital cytometry-based scanning and detection system for malaria has shown promising results in previous studies. 10,11 We tested it for practical feasibility in terms of accuracy, cost effectiveness and rapidity. Our study has shown that in comparison to gold standard microscopy, the digital cytometry gave similar results (in terms of accurate species detection as well) when parasite concentration was on higher side (mean 0.0515%).…”

Section: Rapid Diagnostic Tests (Rdt) Vs Digital Cytometrymentioning

confidence: 99%

“…At the time of publication device didn't had the algorithms to identify mixed infection. 11 Since then the data has accumulated and the sensitivity and specificity has improved in terms of detection of mixed infections. It is very important to note that all three modalities -microscopy, digital cytometry and RDT usage was done in practical scenarios.…”

Section: Rapid Diagnostic Tests (Rdt) Vs Digital Cytometrymentioning

confidence: 99%

Effect of Artificial Intelligence-Based Technology in Malaria Diagnosis

Kumar¹,

Bhavsar²

2019

jebmh

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BACKGROUNDMalaria diagnosis remains a key issue in remote area. The gold standard microscopy has limitations of its own and the commonly used rapid diagnostic tests have variable results. An artificial intelligence based digital cytometry platform was assessed for its comparison with microscopy and rapid diagnostics test in terms of accuracy and feasibility. METHODS100 patients with complaints of fever with chills were included in the study. They were screened for malaria infection by microscopy of thin peripheral smear, antigen based rapid diagnostic test and digital cytometry. RESULTSAt higher malaria parasite concentration, the results between microscopy and digital cytometry were matching exactly. But at lower parasite concentration microscopy had lower sensitivity than digital cytometry. When compared to rapid diagnostic tests the digital cytometry had advantage of better species identification and better sensitivity. CONCLUSIONSIn our study, digital cytometry has greater sensitivity at lower parasite concentration as compared to rapid diagnostic test and microscopy. It was more accurate in species identification as compared to rapid diagnostic test.

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“…Recently, our group published two studies showing that a prototype of such a system could be successful. Srivastava et al presented the prototype system (P1), which showed performance results comparable to those of many marketed RDTs (18). A follow-up article introduced the improved system (P2), which achieved sensitivity, specificity, and species identification results comparable to those of expert human microscopists (19).…”

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confidence: 99%

Evaluation of the Parasight Platform for Malaria Diagnosis

Eshel¹,

Houri-Yafin²,

Benkuzari³

et al. 2017

J Clin Microbiol

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The World Health Organization estimates that nearly 500 million malaria tests are performed annually. While microscopy and rapid diagnostic tests (RDTs) are the main diagnostic approaches, no single method is inexpensive, rapid, and highly accurate. Two recent studies from our group have demonstrated a prototype computer vision platform that meets those needs. Here we present the results from two clinical studies on the commercially available version of this technology, the Sight Diagnostics Parasight platform, which provides malaria diagnosis, species identification, and parasite quantification. We conducted a multisite trial in Chennai, India (Apollo Hospital [n ϭ 205]), and Nairobi, Kenya (Aga Khan University Hospital [n ϭ 263]), in which we compared the device to microscopy, RDTs, and PCR. For identification of malaria, the device performed similarly well in both contexts (sensitivity of 99% and specificity of 100% at the Indian site and sensitivity of 99.3% and specificity of 98.9% at the Kenyan site, compared to PCR). For species identification, the device correctly identified 100% of samples with Plasmodium vivax and 100% of samples with Plasmodium falciparum in India and 100% of samples with P. vivax and 96.1% of samples with P. falciparum in Kenya, compared to PCR. Lastly, comparisons of the device parasite counts with those of trained microscopists produced average Pearson correlation coefficients of 0.84 at the Indian site and 0.85 at the Kenyan site.

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Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria

Cited by 19 publications

References 11 publications

An Enhanced Computer Vision Platform for Clinical Diagnosis of Malaria

An Enhanced Computer Vision Platform for Clinical Diagnosis of Malaria

Effect of Artificial Intelligence-Based Technology in Malaria Diagnosis

Evaluation of the Parasight Platform for Malaria Diagnosis

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