Noul, a Korean diagnostic solution provider, said it published two studies regarding miLab, its AI diagnostic technology for malaria detection, as a single paper in Frontiers, an international scientific journal.

Noul's miLab is the only digital microscopy-based diagnostic solution included in the WHO malaria guidelines.

“The two papers validate miLab's AI diagnostic capabilities through performance verification tests and clinical study and the overall percent agreement (OPA) with local microscopy experts conducted in Malawi, Africa,” the company said.

OPA is a measure used in epidemiology to determine the level of agreement between two or more diagnostic tests

The detection performance was verified using 15 malaria clinical specimens. Analysis was conducted on 200 Field of View (FoV) slides from each blood smear, comparing miLab's results with those obtained through traditional microscopy.

The sensitivity, specificity, and accuracy of miLab were reported as 99.25 percent, 98.1 percent, and 98.86 percent, respectively.

Regarding OPA, the company conducted the study in Mzuzu, Malawi, and evaluated the diagnostic results of 555 suspected malaria patients using miLab, local microscopy, and rapid diagnostic tests.

As a result, miLab exhibited an OPA of 92.21 percent with local microscopy experts. The positive percent agreement (PPA) and negative percent agreement (NPA) were 95.15 percent and 91.43 percent, respectively.

"In malaria-endemic countries, the lack of medical infrastructure, trained microscopy experts, and disease control systems poses significant challenges for malaria eradication,” said Dr. Hans-Peter Beck, a malaria expert from the University of Basel, Swiss Tropical and Public Health Institute, who participated in the study. “Noul's miLab offers a diagnostic solution that combines specimen preprocessing and parasite detection in one device, ensuring accurate and consistent results even in resource-limited settings.”

This innovation could set a new paradigm in AI-based malaria diagnostics, bringing the global community closer to global malaria eradication, Beck added.

Noul R&D Director Choi Kyung-hak also said, "Noul miLab is the first on-device AI diagnostic solution in the microscopy field to integrate specimen preprocessing functions.”

The automated processes for blood smear preparation and cell staining, coupled with deep learning algorithms embedded in the device, allow for simultaneous malaria infection detection, Choi added.

Choi stressed that the results are immediately viewable on the device screen and viewer, enabling on-site and remote malaria diagnostics even in areas with limited medical and IT infrastructure.