Deep learning-based diagnosis of Dirofilaria immitis microfilariae in dog blood

Dirofilaria immitis (D. immitis) or Heartworm is the most pathogenic filariae in dogs which also occasionally infects humans. Dirofilariasis has been found all over the world, and in Iran, on average, 11.5% of dogs are infected. Microscopic examination, the modified Knott method, is a definitive and very common diagnosis method for detecting microfilariae in peripheral blood. It is inexpensive, relatively quick, and does not require advanced and expensive laboratory equipment. However, identification and differentiation of microfilariae from artifacts stand on the abilities and expertise of technicians. The aim of this study was to remove this limitation by developing an artificial intelligence, deep learning-based system that detects microfilariae in blood slides and differentiates microfilaria from thread-like artifacts automatically. To this end, blood samples (n=300) were obtained from stray dogs in Guilan province. The existence of microfilariae was assessed by modified Knott's test under microscopic examinations which identified 29 cases infected with microfilaria. These positive results were confirmed with conventional PCR. The Microfilariae measuring found 295.13±14.9 µm in length and 5.8±0.43 µm in width. The images captured of microfilariae and artifacts were applied to educate and test the suggested deep learning-based system. The developed system diagnoses D. immitis with an accuracy of greater than 95% and thus, can be widely used for epidemiological studies. Since the microfilariae can be miss-diagnosed with thread-shaped artifacts, the proposed system plays an effective role in accurate and reliable diagnosis of D. immitis and can be used in field studies.