The study used 613 original photographs collected from mothers who gave birth at the First Hospital of Jilin University in Changchun, China, between March and July 2025. Eligible participants were women aged 18 or older who were lactating and had a nipple injury during their postpartum hospital stay. Two experienced nurses independently assessed each injury using a standardized five-level scale, and a third nurse resolved disagreements. Women were excluded if another dermatological disorder could obscure the injury assessment or if dressings, tattoos, cosmetics or other materials prevented clear observation. The ethics committee approved the protocol, participants provided written informed consent, and the investigators stated that data collection, storage, processing and analysis followed minimization and de-identification procedures. The report did not provide a patient count separate from the number of photographs.

To improve consistency, nurses captured the images with a Fujifilm X-T4 camera fitted with a 60 mm F2.4 R Macro lens. A gray card was used for color calibration, while image stabilization and automatic mode were used to reduce blur and maintain consistent basic settings. The stated acquisition parameters included a shutter speed of 1/60 second, an ISO range of 3,600–6,000, an aperture between F6 and F8, a 60 mm focal length and automatic white balance. Photographs were excluded if they were blurred, overexposed or obstructed by garments, linens or other material that prevented evaluation of the lesion. The grading scale separated stage 0, with an intact surface and normal skin, from stage I, with redness, bruising or swelling despite an intact surface; stage II, involving superficial abrasions, fissures, bleeding, hematoma or ulceration; stage III, involving deep fissures, blisters, deep ulcers or severe erosion; and stage IV, with complete erosion of part of the nipple. Rectangular bounding boxes were added through the Roboflow platform and their labels were subsequently reviewed by a nurse with at least ten years of clinical experience.

The investigators expanded the 613-photograph collection to 2,452 images through online augmentation. All training images were standardized to 640 by 640 pixels and randomly rotated. Modifications to saturation, value and contrast in the HSV color space introduced variation compatible with differences in illumination and color distribution, and random noise was added to improve performance on less clear images. The researchers also introduced task-specific procedures. Lesion-preserving crops were localized more tightly to the nipple for stage 0–II injuries but extended to the areola and breast for stage III–IV lesions. Local contrast enhancement was applied within lesion regions to make early erythema and small fissures more visible, while class-aware sampling increased the probability of including under-represented advanced-stage injuries. The expanded dataset was divided in a 7:2:1 ratio, yielding 1,716 training images, 490 validation images and 245 independent test images. Because these augmented images were derived from the original collection rather than representing additional independent photographs, the test result does not establish performance on a new patient population.

Six single-stage detectors—YOLOX, PICOdet, YOLOv5, RTDETR, YOLOv8 and YOLOv11—were trained for 200 epochs with a batch size of 16 and 640-by-640-pixel inputs. The models used stochastic gradient descent with an initial learning rate of 0.01, weight decay of 0.0005 and momentum of 0.937. Evaluation followed a common COCO-style protocol: a prediction counted as a true positive only when its class matched the reference label and the predicted and reference bounding boxes met the specified intersection-over-union threshold. Precision represented the proportion of predicted injuries that were correct, recall represented the proportion of actual injuries detected, and mAP@0.5 averaged precision across injury classes at an overlap threshold of 0.5. Training, testing and image preparation were conducted with Python 3.8.18, PyTorch 2.1.0 and CUDA 11.8 on a computer with an NVIDIA RTX 4080S graphics card containing 16 GB of memory and an Intel Core i9-13900KF processor.

YOLOv11 led the six-model single-stage comparison. Its precision of 0.85, recall of 0.82, F1 score of 0.83 and mAP of 0.84 were higher than the corresponding values for every alternative. PICOdet ranked second, with precision of 0.78, recall of 0.75, F1 score of 0.76 and mAP of 0.77. YOLOv5 recorded values of 0.75, 0.72, 0.73 and 0.74; YOLOX produced 0.74, 0.71, 0.72 and 0.73; YOLOv8 produced 0.72, 0.69, 0.70 and 0.71; and RTDETR had the lowest single-stage results, with precision of 0.68, recall of 0.65, F1 score of 0.66 and mAP of 0.67. Under the unified mAP@0.5 protocol, the additional baselines were lower: Faster R-CNN reached 0.621, ATSS 0.578, FCOS 0.508, Cascade R-CNN 0.470, CenterNet 0.472 and Libra R-CNN 0.438. For Faster R-CNN and Cascade R-CNN, the corresponding mAP@0.5:0.95 scores were 0.301 and 0.203. These comparisons support YOLOv11 as the best performer within the tested dataset and experimental setup; they do not establish it as the universally best detector for nipple injury or for other clinical image tasks.

The training curves indicated that YOLOv11’s performance rose rapidly and reached a relatively stable plateau after approximately 100 epochs. Its reported recall remained above 0.85 during much of training and sometimes approached 0.9, while precision stayed above 0.85 across most phases and reached 0.9 in some intervals. Distribution-focal, bounding-box and classification losses generally declined and stabilized as training progressed. The confusion analysis reported a recognition rate of 0.96 for normal nipples at stage 0 and 0.87 for stage III injuries. Some stage I and stage IV images were confused, a finding the researchers associated with the visual resemblance of reddened areas in static images. They identified more stage-specific contrast examples, additional texture information and clinical data beyond photographs as possible routes for improving that discrimination. The model’s F1-confidence analysis also showed that overly stringent confidence criteria could sharply reduce the number of accepted detections, illustrating why threshold selection is a performance trade-off rather than a measure of clinical effectiveness.

Ablation experiments indicated that the system’s results depended on several elements rather than architecture alone. YOLOv11’s mAP@0.5 was 0.71 when trained on the 613 unaugmented images, 0.79 with standard geometric and color augmentation, and 0.84 with the stage-aware clinical augmentation pipeline. Resolution also mattered: the score fell to 0.76 at 416 by 416 pixels, reached 0.84 at 640 by 640 and rose only marginally to 0.85 at 800 by 800. Removing distribution focal loss reduced the score to 0.80, disrupting the balance between box and classification losses lowered it to 0.78, and disabling multi-scale detection produced 0.80. The ablation analysis associated the larger decline in stage I–II lesions with the loss of multi-scale feature use. Taken together, these experiments identified augmentation, input resolution, compound loss configuration and multi-scale feature use as contributors to performance. The 640-by-640 setting was described as the best accuracy-efficiency trade-off, while the 800-by-800 setting imposed a higher computational cost for only a small additional gain.

The team used the Ultralytics Hub platform to prepare the optimized YOLOv11 model for distribution through a mobile application, presenting the tool as a possible real-time aid for nurses and patients. Example inference images showed bounding boxes labeled from stage 0 through stage IV, with confidence values from 0.8 to 1.0. The authors argued that such a system could make grading more objective, quantitative and reproducible while potentially reducing the time required for visual assessment. However, the reported work constitutes technical validation rather than a prospective clinical trial of the mobile program. It did not measure whether application-assisted assessment changes nurses’ decisions, reduces assessment time, improves agreement with experts or affects breastfeeding duration. The tool also relied on visual information alone and did not incorporate inputs such as breastfeeding frequency, pain scores, physical examination findings or other multimodal clinical data.

Several limitations constrain how far the findings can be generalized. All photographs came from one hospital and were acquired under standardized conditions by trained investigators, so performance may differ for patients using ordinary cameras, variable lighting, difficult angles or different skin tones. The original sample consisted of 613 photographs without a reported patient denominator, and augmentation increased the image count without adding independent participants. The grading labels were also derived from expert visual assessment, making their consistency relevant to the reference standard. Most importantly, the study evaluated image-based detection and staging against those labels rather than patient benefit or clinical workflow performance. The authors identified multicenter validation, integration of additional clinical information and prospective efficacy evaluations as priorities for future work. Until those steps are completed, the strongest conclusion supported by the research is that YOLOv11 outperformed the tested alternatives on a controlled, single-center nipple-injury image dataset; it remains a potential decision-support candidate rather than a proven replacement for clinical assessment.

Subject of Research: Comparative benchmarking of object-detection models for image-based nipple-injury detection and staging

Article Title: Comparative examination of YOLO models for classification and utilization in nipple injuries

Article References: Zhang, R., Shu, Y., Ding, S., & Gao, Y. (2026). Comparative examination of YOLO models for classification and utilization in nipple injuries. Frontiers in Medicine, 13, Article 1776068. https://doi.org/10.3389/fmed.2026.1776068

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.1776068

Keywords: YOLOv11, nipple injury, medical image analysis, object detection, deep learning, data augmentation, breastfeeding, clinical decision support, single-center study, Comparative, examination, YOLO