The persistent gap between the growing global surgical demand and the trained surgical workforce [1] highlights the need to develop scalable solutions that can enhance training paradigms and optimize workflow management [2]. Computer-assisted surgery (CAS) systems are one approach to address this challenge, with applications in preoperative planning [3], intraoperative guidance [4], and standardized postoperative assessment [5, 6]. The development and validation of these advanced CAS capabilities fundamentally depend on access to large-scale, deeply annotated surgical video datasets that capture procedural phases, instrument-tissue interactions, and technical skill cues [7, 8]. Phacoemulsification cataract surgery is the most common ophthalmic procedure worldwide and the primary intervention for avoidable blindness [9, 10]. This makes it a critical domain for developing data-driven CAS with potential applications in clinical workflows and training [11, 12]. Publicly available datasets for developing CAS in cataract surgery, such as Cataract-1K [13] and CaDIS [14], are limited by their single-center origin and limited annotation scopes [15]. The absence of a multi-source dataset with comprehensive and multi-layered annotations, including objective skill assessments, has limited the development of generalizable multi-task deep learning models [11]. To address this gap, we present the Cataract-LMM (Large-scale, Multi-source, Multi-task) Dataset, a dataset of 3,000 phacoemulsification procedures recorded at two distinct clinical centers (Farabi and Noor Eye Hospitals, Tehran, Iran) between December 2021 and March 2025. The dataset is enriched with four complementary layers of annotations on subsets of the data: 1. Temporal Phase Labels (Phase): Frame-wise annotations for 13 surgical phases across 150 videos to support automated workflow recognition.

Purpose: Automated Surgical Phase Recognition (SPR) uses Artificial Intelligence (AI) to segment the surgical workflow into its key events, functioning as a building block for efficient video review, surgical education as well as skill assessment. Previous research has focused on short and linear surgical procedures and has not explored if temporal context influences experts' ability to better classify surgical phases. This research addresses these gaps, focusing on Robot-Assisted Partial Nephrectomy (RAPN) as a highly non-linear procedure. Methods: Urologists of varying expertise were grouped and tasked to indicate the surgical phase for RAPN on both single frames and video snippets using a custom-made web platform. Participants reported their confidence levels and the visual landmarks used in their decision-making. AI architectures without and with temporal context as trained and benchmarked on the Cholec80 dataset were subsequently trained on this RAPN dataset. Results: Video snippets and presence of specific visual landmarks improved phase classification accuracy across all groups. Surgeons displayed high confidence in their classifications and outperformed novices, who struggled discriminating phases. The performance of the AI models is comparable to the surgeons in the survey, with improvements when temporal context was incorporated in both cases. Conclusion: SPR is an inherently complex task for expert surgeons and computer vision, where both perform equally well when given the same context. Performance increases when temporal information is provided. Surgical tools and organs form the key landmarks for human interpretation and are expected to shape the future of automated SPR.

Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling more realistic, diverse, and interactive simulation environments. In this study, we introduce SurGen, a text-guided diffusion model tailored for surgical video synthesis, producing the highest resolution and longest duration videos among existing surgical video generation models. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Additionally, we assess their alignment to the corresponding text prompts through a deep learning classifier trained on surgical data. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees.

In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the model to focus on high-quality frames during training. Moreover, the attention weights are utilized to identify characteristic high attention frames for each surgical phase, which could further be used for surgery summarization. OperA is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos, outperforming various state-of-the-art temporal refinement approaches.