AI and Rare Diseases ── Rare blood disorders ── 2026-10-15

Flow of a sickle cell detection method from blood images. Depthwise separable convolution yields two lightweight student CNNs; knowledge distillation from teachers such as MobileNetV2 and DenseNet121 trains them; their features are combined with a KNN classifier; Float32 quantization converts the models to TFLite. The hybrid reaches accuracy 95.45% and F1-score 95.02% at 3.96 MB, showing potential for real-time use on a Raspberry Pi 4 Model B. — Image abstract — the whole article on one page (click to enlarge)

1 Rare blood disorders papers. Lead: Hybrid multi-model framework with knowledge distillation for precise and efficient sickle cell detection.

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Rare blood disorders: treatment, trials and AI

Hybrid multi-model framework with knowledge distillation for precise and efficient sickle cell detection.
Frontiers in digital health · 2026-09-29 · Evidence: Methodological development (validation on an image dataset and deployment on an edge device)
Abstract
Accurate and timely detection of sickle red blood cells has a significant impact on the patients' treatment. Developing a computer vision system that is both lightweight and resource-efficient is important for enabling real-time edge-based applications in resource-constrained healthcare environments. This study proposes a hybrid sickle cell detection framework that uses Bi-lightweight Knowledge Distillation (KD)-guided CNNs, combining two lightweight student models from different teacher networks with Machine Learning (ML) classifiers to identify sickle cells with accuracy and efficiency. Using depthwise separable convolution layers, lightweight models are created that drastically lower model complexity. These lightweight models are improved by extracting knowledge from top-performing teacher networks, such as MobileNetV2 and DenseNet121. Using this Knowledge Distillation learning technique, lightweight models can enhance their representational capabilities without adding to their computational complexity. To improve decision-making performance, the Bi-distilled student models are combined with conventional machine learning KNN classifiers to create a hybrid ensemble that combines the advantages of ML-based classification with dual KD-guided CNN-based feature extraction. The Sickle Cell Disease Dataset is used to train and validate the proposed system. The learned student models are then converted into TensorFlow Lite (TFLite) format using Float32 quantization to further reduce computational complexity. In combination with dual KD-guided CNN models and a KNN classifier, the proposed hybrid model achieves a high classification accuracy of 95.45% and an F1-score of 95.02% while being small at only 3.96 MB. Deploying and evaluating the framework on a resource-constrained Raspberry Pi 4 Model B demonstrate its potential for real-time, edge-based, and point-of-care applications.
Journal IF-equivalent: 5.0 (OpenAlex 2-year mean citedness, value as of 2026-10-13, retrieved 2026-10-15; not the official Clarivate IF)
Reference: Reddy MMK, Lathvik CK, R AA, K K. Hybrid multi-model framework with knowledge distillation for precise and efficient sickle cell detection. Front Digit Health. 2026 Sep 29;8:1938284. doi:10.3389/fdgth.2026.1938284.
Checked: Abstract only

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