Name: Hybrid (Detection and Classification) as Model Testing to Identify and Classify the Protozoan Trypanosome Species
Uploaded: 2023-10-27T13:50:00
Description: Watch this Scientific Journal Video about Hybrid Detection and Classification as Model Testing to Identify and Classify the Protozoan Trypanosome Species at JoVE.com

hybrid (detection and classification) as model testing to identify and classify the protozoan trypanosome species

KI-Programm zum Nachweis und zur Klassifizierung von Trypanosomiasis

Trypanosomiasis is a significant challenge to global health issues due to a variety of zoonotic species causing human disease with a wide range of geographical distribution outside the African and American continents, such as South and Southeast Asia1,2,3. Human African trypanosomiasis (HAT) or sleeping sickness, is caused by Trypanosoma brucei gambiense and T. b. rhodesiense which produce the chronic and acute forms, respectively, representing the major spread in Africa. The causative parasite belongs to the Salivaria group due to the transmission by infected saliva of Tsetse flies4. Whereas, the well-known American trypanosomiasis (Chagas&#39;s disease) caused by T. cruzi has been a public health concern for non-endemic countries; including Canada, the USA, Europe, Australia, and Japan, because of the frequent migration of individuals from endemic areas5. The trypanosome infection belongs to the Stercoraria group because it is transmitted by the infected feces of reduviid bugs. The trypanosomiases and trypanosomoses (Surra disease) caused by the T. evansi infection are endemic in Africa, South America, Western and Eastern Asia, and South and Southeast Asian countries3,6. Although human trypanosomiasis caused by the trypanosome has been reported3,4,7,8,9,10,11,12, the route of transmission of the parasite infection is debated: either the mechanical or infected blood through hematophagous insects such as tsetse flies and tabanids or horse flies6,7,8,9,10,12,13,14. No case report has been found in Thailand, however, a high prevalence of the T. evansi infection in dog15, racing horses, and water buffalo in the eastern region has been published16, suggesting an acquired transmission between domestic animals would have occurred. Several atypical human infections caused by animal trypanosomes (T. vivax, T. b. brucei, T. congolense, T. lewisi, and T. evansi) were reported, which are not the classical forms of human trypanosomes17. Awareness about atypical human infections might be underestimated, highlighting the need for improved diagnostic tests and field investigations for detection and confirmation of these atypical cases, and allowing for proper control and treatment of animal pathogenic diseases that affect global livestock, food security18, and human healthcare. This led to the development of a potential strategy integrated with an existing common method (microscopic examination) to rapidly screen blood samples in remote areas during active surveillance, enabling the identification of the hotspot zones for restricting and controlling the disease.
Having a sporadic incidence of the Surra disease in a wide range of domestic animals such as dromedaries, cattle, equines, and dogs that evoke a euryxenous T. evansi may be zoonotic to humans1,4,13,14. Human infection seems impossible because a trypanolytic factor in human serum, expressed from a sra-like gene, is capable of preventing human T. brucei and T. congolense12,19. Furthermore, as the first case report from India demonstrates, the illness has no association with immunocompromised HIV patients4. As described above, the possible human infection may be related to a high-density lipoprotein deficiency with abnormal function of the trypanosome lytic factor, which is a rare autosomal recessive genetic disorder, namely Tangier disease4. In 2016, a Vietnamese patient was discovered to possess two wild-type APOL1 alleles and a serum APOL1 concentration within the normal range. However, the theory of APOL-1 deficiency is no longer considered valid12. Therefore, one possible mechanism of trypanosome infection is direct contact of a wound with infected animal blood during occupational animal farming4,12. Microscopic examination reveals that T. evansi morphology is a monomorphic form of the trypomastigote including a predominant long slender, flagellated, and dividing trypanosome which is similar to their relative species of T. brucei1,12,13. The nucleus is in the central position with a visible small kinetoplast in the posterior position. A previous study indicated that the parasite can exist in two comparable forms, known as the classical and truncated forms. However, it remains necessary to confirm their respective pathogenic effects on hosts20. The course of symptoms varies ranging from intermittent fever associated with chills and sweating. Suramin, fortunately, is a successful first-line therapy for early-stage human African trypanosomiasis with no invasion of the central nervous system (CNS), healing patients in India and Vietnam4,12,21.
Except for clinical sign examination, several diagnostic methods for T. evansi parasites exist, including parasitological microscopic observation4,9,12, serological4,8,9,10,12, and molecular biological tests4,12. Thin-blood films stained with Giemsa are often used to visualize the parasite present under microscopic examination, which is routinely and commonly used22. However, the procedure appears to be feasible; nonetheless, it is time-consuming and labor-intensive, has inter-rater assessment variability, is sensitive to only an acute phase, and requires a personal trainee23. Both molecular biology and serological testing also needed highly skilled personnel to perform multiple processes of sample preparation, including extracting and purifying the samples before testing them with expensive apparatus, which is difficult to standardize, risk of contamination with extra-parasitic materials, and discrepancies in results24. Based on the rationale described above, rapid and early screening technology is needed to support the field surveillance study and ensure that the survey result is reported in a timely manner to identify the hotspot zone for further control of the disease transmission1,8. Computerized-based devices (CAD) have been proposed as an innovative technology for medical fields, including histopathological and cytopathological tasks25. The CAD mentioned above was performed at high speed and computed using pattern recognition, namely, artificial intelligence (AI). The AI method is accomplished using convolutional neural network algorithms that can be used to deal with a large number of dataset samples, especially, a supervised learning approach that trains a well-trained model upon data consumption.
In general, AI is the ability of computers to solve tasks that require expert intelligence, such as data labeling. Machine learning (ML), a subfield of AI, is represented as a computer system with two different processes comprised of feature extraction and pattern recognition. Deep learning (DL), or advanced ML algorithms, refers to the development of computerized programs and devices comparing human-like performance with levels of accuracy greater and equal to that accomplished by human professionals26. Currently, the role of DL in medical and veterinary fields is promisingly expanding and revolutionizing communicable disease prevention with the aim of recent prevention and guiding it to individual health staff22,27. The potential DL application is limitless with quality labels and a large number of augmented datasets, freeing specialists to manage the project task. Specifically, an advance in the digital image along with computer-assisted analysis, improved the automatic diagnostic and screening in five categories of pathology reported; including static, dynamic, robotic, whole slide imaging, and hybrid methods28. It is necessary to consider that the integration of DL algorithm approaches and digital image data could encourage local staff to utilize the technology in their daily practices.
Previously, the increase in prediction accuracy of using a hybrid model had been proven27. To identify the trypanosome parasite in microscopic images, this research presents two hybrid models, incorporating the YOLOv4-tiny (object detection) and Densenet201 (object classification) algorithms. Among several detection models, YOLOv4-tiny with a CSPDarknet53 backbone showed high performance as a prediction result in terms of localization and classification29. Since the real-time detector has modified the optimal balance among the input network resolution, the amount of the convolutional layer, the total parameter, and the number of layer outputs, it has improved prioritizing fast operating speeds and optimizing for parallel computations when compared to previous versions. Dense Convolutional Network (DenseNet) is another popular model that achieves state-of-the-art results across competitive datasets. DenseNet201 yielded a similar validation error comparable to that of ResNet101; however, DenseNet201 has fewer than 20 million parameters, which is less than ResNet101&#39;s more than 40 million parameters30. Therefore, the DenseNet model could improve prediction accuracy with an increasing number of parameters with no sign of overfitting. Here, an artificial intelligence (AI) program utilizes a hybrid deep learning algorithm with deep detection- and classification neural network backbones on the in-house CiRA CORE platform. The developed program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. This technology has the potential to revolutionize disease surveillance and control by providing a rapid, automated, and accurate screening method. It could aid local staff in making more informed decisions on transmission-blocking strategies for parasitic protozoan disease.

Autor im Rampenlicht: KI-gesteuerter Nachweis von Trypanosomenspezies aus mikroskopischen Bildern 

Überlegene automatische Identifizierung von Trypanosomenparasiten durch Verwendung eines hybriden Deep-Learning-Modells

The goal of the research is to develop an artificial intelligence AI model that makes use of a hybrid deep learning technique of object detection and classification backbones for defining the protozoa trypanosome specie, namely trypanosoma cruzi, T.brucei, and T.evansi, from oil immersion, microscopic images on the in-house, low-code AI platform CiRA CORE. The most recent development in our field of research is the development of the learning algorithms that can identify and classify trypanosome species from microscopic images. This program has the potential to revolutionize the surveillance and control by providing a rapid automated and accurate screening method that can be used by local staff in remote areas.Using the modified and hybrid algorithms between two different deep learning models within the purpose AI program can overcome many challenges. Identification of shared morphology, mixed and immature infection, and precise species characterization enables automatic standard taxonomy.

Watch this Scientific Journal Video about Hybrid Detection and Classification as Model Testing to Identify and Classify the Protozoan Trypanosome Species at JoVE.com

Hybrid (Detection and Classification) as Model Testing to Identify and Classify the Protozoan Trypanosome Species  

Hybrid (Detektion und Klassifikation) als Modellversuch zur Identifizierung und Klassifizierung der Protozoen-Trypanosomen-Spezies

Navigieren Sie zunächst zur allgemeinen Symbolleiste, klicken Sie auf Schaltfläche Ausführen und dann auf Debuggen, um das Hybridtestmodell zu starten. Wechseln Sie als Nächstes zur Bildsymbolleiste, wählen Sie "Erfassung" und dann "Bildfolie". Gehen Sie zu Deep Composite und klicken Sie auf Deep D-C.Bevor Sie mit der Testphase für die Bilder beginnen, klicken Sie auf die Einstellungsoption, die im Bildfolien-Tool verfügbar ist, und importieren Sie die Testbilder. Klicken Sie dann auf die Einstellungsoption im Tiefen-D-C-Tool, um die beiden gespeicherten Trainingsgewichtsdateien zu importieren. Klicken Sie für die Funktion "Erkennen" auf die Schaltfläche "Config", die Plus-Schaltfläche und wählen Sie die Backend-CUDA oder -CPU aus.Geben Sie einen Namen ein, und klicken Sie auf OK. Wählen Sie das Verzeichnis der Gewichtsdatei aus und klicken Sie auf Auswählen. Klicken Sie für die Classif-Funktion auf die Schaltfläche Config, gefolgt von der Plus-Schaltfläche, und wählen Sie dann das Backend aus. Geben Sie einen Namen ein, und klicken Sie auf OK. Wählen Sie anschließend das Verzeichnis für die Gewichtsdatei aus und klicken Sie auf Auswählen.Um die Ergebnisse des Testbilds anzuzeigen, klicken Sie auf die Bildfunktion, die im Debug-Tool verfügbar ist. Um die vorhergesagten Ergebnisse für jedes Bild auszuwerten, klicken Sie abschließend auf die Schaltfläche Ausführen im Werkzeug Schaltflächenausführung.

Research

JoVE Journal

Bioengineering

Hybrid (Detection and Classification) as Model Testing to Identify and Classify the Protozoan Trypanosome Species

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of ...

Forschung

Biotechnik

Training Process CiRA CORE Platform and Object Detection Model Evaluation to Identify and Classify the Protozoan Trypanosome Species

Evaluierung der CiRA CORE-Plattform und des Objekterkennungsmodells zur Identifizierung und Klassifizierung der Protozoen-Trypanosomenarten

Image Classification as Model Training and Model Evaluation to Identify and Classify the Protozoan Trypanosome Species

Bildklassifizierung als Modelltraining und Modellauswertung zur Identifizierung und Klassifikation der protozoischen Trypanosomenarten