Deep Learning for Instrument Detection and Assessment of Operative Skill in Surgical Videos

Lam, Kyle; Lo, Frank P.-W.; An, Yujian; Darzi, Ara; Purkayastha, Sanjay; Lo, Benny

doi:10.1109/tmrb.2022.3214377

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Two of the 50 studies [ 34 , 61 ] included in this review were found to match the inclusion criteria during the process of full-text screening and were thus included through cross-referencing. Six studies [ 21 , 29 , 37 , 45 , 55 , 58 ] were obtained during the re-run prior to final analysis six months after the initial literature search and 13 [ 13 , 17 , 34 , 36 , 38 , 42 , 48 , 50 , 52 – 54 , 56 , 61 ] during the second update on May 31st, 2023. Table 1 gives an overview of the 50 studies included in this systematic review (for full information extracted see Supplementary Table S3).…”

Section: Resultsmentioning

confidence: 99%

“…Eleven studies (22%) [ 15 , 28 , 33 , 34 , 38 , 39 , 41 , 43 – 45 , 52 ] used data of real surgical procedures. Eight [ 15 , 28 , 33 , 38 , 39 , 44 , 45 , 52 ] of them using videos of laparoscopic surgeries as for example laparoscopic cholecystectomies [ 28 , 39 ] or laparoscopic pelvic lymph node dissections [ 15 ]. Three studies [ 34 , 41 , 43 ] used video data obtained from robotic surgeries such as robotic prostatectomy [ 41 ] or robotic thyroid surgery [ 43 ].…”

Section: Resultsmentioning

confidence: 99%

“…Four different types of input data were used throughout the 50 studies: video data ( n = 25, 50%) [ 12 , 13 , 15 , 21 , 24 , 25 , 27 , 28 , 31 – 34 , 36 , 38 , 39 , 41 – 45 , 50 – 52 , 55 , 58 ], kinematic data ( n = 22, 44%) [ 14 , 16 – 20 , 22 , 23 , 29 , 35 , 37 , 40 , 46 – 49 , 54 , 56 , 57 , 59 – 61 ], eye tracking data ( n = 2) [ 36 , 53 ], and functional near-infrared spectroscopy (fNIRS) data ( n = 2) [ 26 , 30 ]. Video recordings either from laparoscopic/robotic cameras or external cameras are used in 25 studies (50%).…”

Section: Resultsmentioning

confidence: 99%

“…One study [ 28 ] extended the publicly available m2cai16-tool dataset [ 66 ] with locations of surgical tools and published it as m2cai16-tools-localisation dataset. Though, most studies ( n = 28, 56%) [ 12 , 15 , 18 , 21 , 24 , 26 , 27 , 30 , 31 , 33 – 36 , 38 – 44 , 46 , 49 , 51 – 54 , 57 , 58 ] created private datasets, that were not publicly released. Most datasets ( n = 46, 92%) [ 12 – 23 , 25 – 32 , 35 – 40 , 42 – 61 ] were monocentric.…”

Section: Resultsmentioning

confidence: 99%

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Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review

et al. 2023

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Background Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery. Methods A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Results In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB. Conclusion AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review

et al. 2023

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“…The investigations carried out in recent years, which mark the state-of-the-art, regarding the detection of movement in a video signal, have not only been used for presence monitoring and video surveillance [23][24]. Different applications have been made using this technology among which are the following: health care and attention, surgical interventions [25], detection of epileptic seizures in neonatal intensive care units [26], cerebral palsy treatments in infants [27], and motion of vehicles and pedestrians monitoring [28], among others. Similarly, advanced research in this field makes use of models based on artificial intelligence, deep learning, convolutional neural networks [29], and change detection in multitemporal monitoring images under low illumination [30].…”

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confidence: 99%

Motion detection using a Raspberry Pi 4 and OpenCV

Luna¹,

Rangel²,

Aceves³

et al. 2023

IJLEMR

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Currently, one of the most important parts of the cloud and computing platforms are the data centers, since they house the equipment where the applications and information processing are executed. The vitalkey areas of data centers are the equipment sites. In addition to the fact that access is controlled and restricted to these areas, continuous monitoring of the presence of people in these areas is critical. The data centers have closed circuit television systems that allow this activity to be carried out; however, these systems do not send alert messages that allow timely reaction to any threat from unauthorized persons who have accessed the equipment site. Similarly, presence detectors of different technologies are used, which present disadvantages compared to the system presented here. For these reasons, in this work a motion detection system was developed based on the processing of video images captured with a camera integrated into a Raspberry Pi 4 card and programmed with Python and OpenCV functions. When the system detects movement, it sends a WhatsApp alert message to a mobile phone. The tests carried out showed that the system is accurate to 98.08%.

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CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection

Zhao,

Guo,

et al. 2023

Health Inf Sci Syst

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Deep Learning for Instrument Detection and Assessment of Operative Skill in Surgical Videos

Cited by 6 publications

References 22 publications

Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review

Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review

Motion detection using a Raspberry Pi 4 and OpenCV

CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection

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