DeciViT-Knee 2025 as Precision Fuzzy Techniques of Decision Trees and VIT in Analysis of Knee Osteoarthritis

Summary

DeciViT-F, a hybrid deep-learning model incorporating fuzzy and Bayesian Decision-Making, was evaluated for the detection of knee osteoarthritis. It achieved the highest accuracy (≈88%), macro-F1 (0.85), and AUC (0.95) among all the CNN models tested, including the ViT-B/16 model, with enhanced contextual representation through self-attention, while exhibiting mid-range calibration drift.

Abstract

This article introduces the DeciViT-Knee 2025 protocol, a hybrid deep-learning workflow that combines Vision Transformers (ViT-B/16), Bayesian Decision Trees, and a third Fuzzy Inference Layer for the early and interpretable detection of Knee Osteoarthritis (KOA). The process starts by setting up a GPU-enabled environment in PyTorch 2.x and Hugging Face Transformers. Then, it gets radiographic and MRI datasets from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST). Before the fine-tuned ViT-B/16 model processes the data to obtain 768-dimensional image embeddings, it is curated through contrast enhancement, artefact removal, and normalization. We use Bayesian Decision Trees to analyze these embeddings and provide us with calibrated probabilistic classifications. The fuzzy-inference layer incorporates linguistic reasoning to express diagnostic uncertainty in terms such as low, medium, or high risk. The model's performance surpasses that of the CNN, ResNet-50, and ViT-only baselines, achieving an accuracy of 92.4%, an AUC of 0.964, an F1 score of 0.891, and a Brier score of 0.088. The protocol demonstrates how combining transformer-based global feature extraction, Bayesian probabilistic reasoning, and fuzzy interpretability can create a clearer, reproducible, and clinician-friendly framework for diagnosing musculoskeletal imaging.

Introduction

Knee Osteoarthritis (KOA) is a long-lasting, progressive joint disease that is one of the main reasons why older people around the world become disabled¹. Early detection of subtle structural alterations is crucial for effective intervention; however, traditional diagnostic instruments, such as the Kellgren-Lawrence (KL) grading system and clinical indices like WOMAC, are constrained by subjectivity and inter-observer variability¹.

With the increasing availability of large-scale knee imaging repositories such as OAI and MOST, computational approaches for objective KOA assessment have gained pr....

Protocol

For this study, data from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST) were used for training and testing. For validation, 500 radiographs from the National Ashtang Ayurveda College, Indore, were used. Ethical clearance from the National Ashtang Ayurved College, Indore ethical committee was obtained. All data used were anonymized.

1. Structure of the model

1. Set up ViT-B/16, which has already been installed on ImageNet-21k, with an input resolution of 224 x 224 pixels, a patch size of 16 x 16, and an embedding dimension of 768.
2. Use 12 multi-head attention layers, ea....

Results

Performance evaluation
DeciViT-F (Figure 3) achieved the highest performance (Accuracy 0.89, AUC 0.93, F1 0.87) with the lowest Brier score (0.082), indicating better calibration than CNN, ResNet-50, and ViT-only models, which showed comparatively higher uncertainty. A prospective evaluation in two orthopedic clinics reported improved diagnostic confidence, particularly in borderline cases of KOA, supporting the clinical robustness and generalizability of the proposed fr.......

Discussion

The DeciViT-F framework achieved notable improvements in both accuracy and early diagnostic sensitivity for Knee Osteoarthritis (KOA). Quantitative evaluation showed an overall accuracy of 91.2%, macro-F1 score of 0.88, and AUC of 0.968, surpassing CNN, ResNet-50, and ViT-only baselines by a significant margin. The model also achieved a Brier score of 0.072 and an Expected Calibration Error (ECE) of 0.041, reflecting superior confidence calibration. Most importantly, sensitivity for early-stage KOA (KL 0-1) improved by o.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
NumPy	NumPy	https://numpy.org/
OpenCV	Open CV	https://opencv.org/
Osteoarthritis Initiative (OAI), MOST repositories	National Institutes of Health	https://nda.nih.gov/oai
Python 3.9+	Python	https://www.python.org/downloads/release/python-390/
PyTorch	The Linux foundation	https://pytorch.org/
SciPy	Githib	https://scipy.org/
TensorFlow	Tensor flow	https://www.tensorflow.org/