Our app
for veterinarians
and soon pet owners
Our app offers a comprehensive tool for recording and documenting standardized findings and also serves as a digital patient file. It has an integrated photo and video function that enables visual documentation. In addition, it supports the efficient management of animal data and functions as an interactive information tool that ensures better overview and organization in everyday practice.
Our app
for veterinarians
and soon pet owners
Our app
for veterinarians
and soon pet owners
Our app
for veterinarians
and soon pet owners
Our app
for veterinarians
and soon pet owners
Our app
for veterinarians
and soon pet owners
Version 1.1.12
November 2024
The app is currently in the testing phase.
Apple App Store and Google Play Store
integration are in progress .
Standardized digital patient record
Networking of clinical data
Training of artificial intelligence
Evidence for the benefit of patients can be achieved through the standardized collection of clinical data in text and images in a digital patient file and a high degree of automation in evaluation and communication (saving time, personnel and costs).
Our app
for veterinarians
and soon pet owners
Our app offers a comprehensive tool for recording and documenting standardized findings and also serves as a digital patient file. It has an integrated photo and video function that enables visual documentation. In addition, it supports the efficient management of animal data and functions as an interactive information tool that ensures better overview and organization in everyday practice.
Our primary goal is to enable the standardized recording of clinical data in everyday life. This leads to improved communication with both animal owners and colleagues. It also creates the basis for long-term studies and the systematic review of the effectiveness of treatment measures. The secure collection of meaningful statistics on one's own patient collective promotes transparency and provides a good basis for decisions on clinical patients.
Standardized digital patient record
Networking of clinical data
Training of artificial intelligence
Our primary goal is to enable the standardized recording of clinical data in everyday life. This leads to improved communication with both animal owners and colleagues. It also creates the basis for long-term studies and the systematic review of the effectiveness of treatment measures. The secure collection of meaningful statistics on one's own patient collective promotes transparency and provides a good basis for decisions on clinical patients.
Our app
for veterinarians
and soon pet owners
Artificial intelligence will change everyday veterinary practice. This technology can be used positively and be an aid for veterinarians. By analyzing complex medical data, examinations can be made more objective and therapies optimized. Our goal is to use this technology and make it accessible for the benefit of pet owners, veterinarians and patients.
Evidence for the benefit of patients can be achieved through the standardized collection of clinical data in text and images in a digital patient file and a high degree of automation in evaluation and communication (saving time, personnel and costs).
May A., Gesell-May S., Müller T., Ertel W.
DOI: 10.1111/evj.13528
May A., Gesell-May S., Müller T., Ertel W.
DOI: 10.1111/evj.13528
Abstract
Background
Due to recent developments in artificial intelligence, deep learning, and smart-device-technology, diagnostic software may be developed which can be executed offline as an app on smartphones using their high-resolution cameras and increasing processing power to directly analyze photos taken on the device.
Objectives
A software tool was developed to aid in the diagnosis
of equine ophthalmic diseases, especially uveitis.
Study design
Prospective comparison of software and clinical diagnoses.
Methods
A deep learning approach for image classification was used to train software by analyzing photographs of equine eyes to make a statement on whether the horse was displaying signs of uveitis or other ophthalmic diseases. Four base networks of different sizes (MobileNetV2, InceptionV3, VGG16, VGG19) with modified top layers were evaluated. Convolutional Neural Networks (CNN) were trained on 2346 images of equine eyes, which were augmented to 9384 images. 261 separate unmodified images were used to evaluate the performance of the trained network.
Results
Cross validation showed accuracy of 99.82% on training data and 96.66% on validation data when distinguishing between three categories (uveitis, other ophthalmic diseases, healthy).
Main limitations
One source of selection bias for the artificial intelligence presumably was the increased pupil size, which was mainly present in horses with ophthalmic diseases due to the use of mydriatics, and was not homogeneously dispersed in all categories of the dataset.
Conclusion
Our system for detection of equine uveitis is unique and novel and can differentiate between uveitis and other equine ophthalmic diseases. Its development also serves as a proof-of-concept for image-based detection of ophthalmic diseases in general and as a basis for its further use and expansion.
connecting care
Our app
for veterinarians
and soon pet owners
Our app offers a comprehensive tool for recording and documenting standardized findings and also serves as a digital patient file. It has an integrated photo and video function that enables visual documentation. In addition, it supports the efficient management of animal data and functions as an interactive information tool that ensures better overview and organization in everyday practice.
Vereinfachung Langzeitstudien
- 2022
Wir arbeiten daran, dass es für Forschende deutlich leichter als bisher möglich ist, Langzeitdaten zu erheben und damit Behandlungsmethoden auf ihre Wirksamkeit zu prüfen.
Diagnose von über 14 Erkrankungen des Pferdeauges - 2023
Wir trainieren bereits eine künstliche Intelligenz, die in der Lage ist, über 14 Erkrankungsgruppen des Pferdeauges zu diagnostizieren.
Epidemiologische Daten und Vorberichte vollständig digital abrufbar - 2024
Tierärztinnen und Tierärzte können zu Beginn der Untersuchung auf einfache Art und Weise epidemiologische Angaben und den Vorbericht direkt am Patienten in der digitalen Patientenakte abrufen.
Standardisierte Protokolle - Auge 2022, weitere - 2025
Wir möchten, dass Tierärztinnen und Tierärzte Untersuchungsprotokollen folgen können. Von Anfang an wird dadurch ermöglicht, ihre Fälle für Weiterbildungen im Alltag zu sammeln und jederzeit abzurufen.
Leichtere Kommunikation - 2025
Mit der Befundung in der anirec-App werden Fotos und Videos direkt in der digitalen Patientenakte gespeichert. Diese Datei kann aktuell als PDF z.B. an Besitzer*in, überweisende Kolleginnen und Kollegen gesendet werden. Bald wird dies innerhalb der App durch einfache Freischaltung möglich sein.
Statistiken der eigenen Arbeit - 2026
Wir entwickeln ein Tool, mit dem statistisch korrekt die Erfolgsquoten der eigenen Behandlungen messbar sind. Damit wollen wir einen Betrag zur evidenzbasierten Medizin leisten.
Lahmheitserkennung und Pain Score - 2026
Wir arbeiten daran, dass unsere App anzeigt, ob ein Pferd lahm ist oder akut Schmerzen äußert und wie stark diese Schmerzäußerungen ausgeprägt sind.
Schnelle Dokumentation und Erstellung der Arztbriefe - 2027
Unser Ziel ist, die Dokumentation und die Erstellung des Arztbriefes stark zu beschleunigen und dadurch Tierärztinnen und Tierärzte zu entlasten. Patient und Besitzer*in profitieren von aussagekräftigen schriftlichen Angaben, die jederzeit und überall digital abrufbar sind.
Agenda
Scharre A., Scholler D., Gesell‐May S., Müller T., Zablotski Y., Ertel W., May A.
DOI: 10.1111/evj.14087
Scharre A., Scholler D., Gesell‐May S., Müller T., Zablotski Y., Ertel W., May A.
DOI: 10.1111/evj.14087
Abstract
Background
Lameness in horses is a long-known issue influencing the welfare, as well as the use, of a horse. Nevertheless, the detection and classification of lameness mainly occurs on a subjective basis by the owner and the veterinarian.
Objectives
The aim of this study was the development of a lameness detection system based on pose estimation, which permits non-invasive and easily applicable gait analysis. The use of 58 reference points on easily detectable anatomical landmarks offers various possibilities for gait evaluation using a simple setup.
Study design
For this study, three groups of horses were used: one training group, one analysis group of fore and hindlimb lame horses and a control group of sound horses. The first group was used to train the network; afterwards, horses with and without lameness were evaluated.
Results
The results show that forelimb lameness can be detected by visualizing the trajectories of the reference points on the head and both forelimbs. In hindlimb lameness, the stifle showed promising results as a reference point, whereas the tuber coxae were deemed unsuitable as a reference point.
Conclusion
The study presents a feasible application of pose estimation for lameness detection, but further development using a larger dataset is essential.
About us
Architektur
der Platform
Tobias Müller
Geschäftsführung
Entwicklung App & KIs
Cyber-Security
Software-Developer
Vernetzung
klinischer Daten
Dr. Stefan Gesell-May
Geschäftsführung
Veterinärmedizinische Inhalte
Forschung & Entwicklung
Praktizierender Tierarzt
Kommunikation
der Marke
Elias Escotto
Brand Management
Corporate Design
Future UI / UX
Gestalter
nondual.studio
