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.

Hematology analyzers capable of performing complete blood count (CBC) have lagged in their prevalence at the point-of-care. Sight OLO (Sight Diagnostics, Israel) is a novel hematological platform which provides a 19-parameter, five-part differential CBC, and is designed to address the limitations in current point-of-care hematology analyzers using recent advances in artificial intelligence (AI) and computer vision.Accuracy, repeatability, and flagging capabilities of OLO were compared with the Sysmex XN-Series System (Sysmex, Japan). Matrix studies compared performance using venous, capillary and direct-from-fingerprick blood samples. Regression analysis shows strong concordance between OLO and the Sysmex XN, demonstrating that OLO performs with high accuracy for all CBC parameters. High repeatability and reproducibility were demonstrated for most of the testing parameters. The analytical performance of the OLO hematology analyzer was validated in a multicenter clinical laboratory setting, demonstrating its accuracy and comparability to clinical laboratory-based hematology analyzers. Furthermore, the study demonstrated the validity of CBC analysis of samples collected directly from fingerpricks.

Hematology analyzers capable of performing complete blood count (CBC) have lagged in their prevalence at the point-of-care. Sight OLO® (Sight Diagnostics, Israel) is a novel hematological platform which provides a 19 parameter, five-part differential CBC, and is designed to address the limitations in current point-of-care hematology analyzers using recent advances in artificial intelligence (AI) and computer vision. Accuracy, repeatability, and flagging capabilities of OLO were compared with the Sysmex XN-Series System (Sysmex, Japan). Matrix studies compared performance using venous, capillary and direct-from-finger-prick blood samples. Regression analysis shows strong concordance between OLO and the Sysmex XN, demonstrating that OLO performs with high accuracy for all CBC parameters. High repeatability and reproducibility were demonstrated for most of the testing parameters. The analytical performance of the OLO hematology analyzer was validated in a multicenter clinical laboratory setting, demonstrating its accuracy and comparability to clinical laboratory-based hematology analyzers. Furthermore, the study demonstrated the validity of CBC analysis of samples collected directly from fingerpricks.

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.