opscribe-ai/OpenSurgery_Ortho

OpenSurgery Ortho

A multimodal fine-tuning dataset pairing sequences of surgical video keyframes with surgeon narration transcripts, built to train vision-language models (VLMs) on orthopedic surgical captioning.

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Dataset Summary

Split	Records	Source
Train	2,417	90% stratified by video
Val	265	10% stratified by video
Total	2,682	8 surgical videos

Each record contains 5 keyframes extracted from a 15-second sliding window (5-second stride) paired with the surgeon's narration text for that time interval.

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Procedures Covered

Video	Procedure	Records
TKA	Total Knee Arthroplasty	292
TKA_2	Total Knee Arthroplasty (case 2)	257
HipArthoplasty	Total Hip Arthroplasty	392
THA_2	Total Hip Arthroplasty (case 2)	426
THA_3	Total Hip Arthroplasty (case 3)	273
HipResurfacing	Hip Resurfacing	483
HipResurface_2	Hip Resurfacing (case 2)	254
BigToeFusion	MTP Arthrodesis (Big Toe Fusion)	305

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Dataset Versions

finetune_v2 (current, recommended)

High-quality multimodal dataset with 5 frames per window. Text sourced from cleaned structured transcripts (surgeon narration), with faster-whisper fallback for segments not covered by the transcript. Built with a rigorous quality filter chain.

finetune_v1 (legacy)

Single-frame dataset with simpler windowing. Superseded by v2.

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How It Was Created

Step 1 — Transcription

Each surgical video has a structured transcript written by the operating surgeon narrating what they are doing in real time. These were cleaned and aligned to video timestamps using faster-whisper (large-v3, word-level timestamps, language=en).

Step 2 — Windowing

A sliding window of 15 seconds with 5-second stride is swept across each video's word-level timestamp timeline. For each window, the overlapping surgeon narration text is extracted by interval-overlap sentence slicing (not midpoint assignment), preserving complete sentences.

Step 3 — Frame Extraction

5 frames are evenly sampled from each 15-second window using OpenCV. Frames are saved as JPEG at native video resolution.

Step 4 — Quality Filtering

Records are dropped if any of the following apply:

• Fewer than 8 words in the narration text
• More than 200 words (likely a runaway window)
• Phrase loop detected — any 3-6 word phrase repeated 5+ times (transcript artifact)
• Non-English text detected
• Non-visual narration — introductory/conceptual text with no surgical content
• Bad frames — any frame that fails to load

Step 5 — Split

90/10 train/val split stratified by video — all records from a given video stay in the same split to prevent data leakage.

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Record Format (JSONL)

Each line in finetune_v2/train.jsonl and finetune_v2/val.jsonl is a JSON object in LLaMA-Factory multi-image format:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a precise visual observer of surgical video..."
    },
    {
      "role": "user",
      "content": [
        {"type": "image", "image": "<path_or_url_to_frame_0>"},
        {"type": "image", "image": "<path_or_url_to_frame_1>"},
        {"type": "image", "image": "<path_or_url_to_frame_2>"},
        {"type": "image", "image": "<path_or_url_to_frame_3>"},
        {"type": "image", "image": "<path_or_url_to_frame_4>"},
        {"type": "text",  "text": "Describe the scene in this surgical video frame..."}
      ]
    },
    {
      "role": "assistant",
      "content": "The surgeon is placing a tibial baseplate trial..."
    }
  ],
  "metadata": {
    "video": "TKA",
    "t_start": 120.0,
    "t_end": 135.0,
    "text_source": "structured"
  }
}

Fields:

• messages — standard chat format compatible with LLaMA-Factory, Axolotl, and HuggingFace TRL
• metadata.video — source video identifier
• metadata.t_start / t_end — window timestamps in seconds
• metadata.text_source — "structured" (surgeon transcript) or "whisper" (ASR fallback)

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How to Use for Fine-tuning

Option A — LLaMA-Factory (native format)

The JSONL files are directly compatible with LLaMA-Factory's multi-image training pipeline.

1. Register the dataset in data/dataset_info.json:

"opensurgery_ortho_train": {
  "file_name": "finetune_v2/train.jsonl",
  "formatting": "sharegpt",
  "columns": {"messages": "messages", "images": "images"}
}

2. Set dataset: opensurgery_ortho_train in your LLaMA-Factory YAML config.

Option B — HuggingFace TRL / SFTTrainer

from datasets import load_dataset
from trl import SFTTrainer, SFTConfig

dataset = load_dataset("opscribe-ai/OpenSurgery_Ortho", data_files={
    "train": "finetune_v2/train.jsonl",
    "validation": "finetune_v2/val.jsonl"
})

Option C — qwen-vl-finetune (Qwen official)

Convert to qwen-vl-finetune JSON format using the provided conversion script:

python3 scripts/convert_finetune_v2.py \
    --v2_dir /path/to/local/finetune_v2 \
    --out_dir /path/to/output \
    --new_prefix /path/to/frames

This produces a JSON array where each record has:

• "image": list of 5 frame paths
• "conversations": [{"from": "human", "value": "..."}, {"from": "gpt", "value": "..."}]

Recommended Training Config

Parameter	Value
Model	Qwen2.5-VL-7B or 72B
LoRA rank	32–64
Epochs	3
Batch size	1 per GPU
Grad accumulation	16
Learning rate	2e-5
Max pixels	576×28×28
Images per sample	5

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Source Videos

Raw surgical videos are included in OpScribe_Ortho_Media/. These are real orthopedic procedures with surgeon narration recorded for educational and research purposes.

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Citation

If you use this dataset, please cite:

@dataset{opensurgery_ortho_2026,
  author    = {OpScribe AI},
  title     = {OpenSurgery Ortho: Surgical Video VLM Fine-tuning Dataset},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/opscribe-ai/OpenSurgery_Ortho}
}

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License

MIT — see LICENSE for details. Clinical use requires independent validation.