Anatomical subject validation of an instrumented hammer using machine learning for the classification of osteotomy fracture in rhinoplasty

Lamassoure, Léo; Giunta, Justine; Rosi, Giuseppe; Poudrel, Anne-Sophie; Méningaud, Jean-Paul; Bosc, Romain; Haïat, Guillaume

doi:10.1016/j.medengphy.2021.08.004

Cited by 9 publications

(8 citation statements)

References 23 publications

(27 reference statements)

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“…However, cracks may appear during rhinoplasty [ 35 ]. We have shown in previous studies corresponding to rhinoplasty [ 27 , 28 , 29 ] that this crack in front of the osteotome tip can be detected by a sudden decrease in the value of τ due to a decrease of the sample rigidity. Therefore, further work is needed to distinguish situations where a decrease in τ is due to (i) the osteotome reaching the end of the sample and (ii) the creation of a crack around the osteotome tip.…”

Section: Discussionmentioning

confidence: 94%

“…The use of an instrumented hammer in osteotomies had already been studied by our group in the context of rhinoplaties [ 27 , 28 ]. In these previous studies, the same hammer was used to detect crack propagation and the change of material properties at the tip of the osteotome.…”

Section: Discussionmentioning

confidence: 99%

“…A dedicated signal processing technique illustrated in Figure 2 and Figure 3 was applied to each rf signal s(t) using information derived from the different peaks obtained in the signal, similarly as in previous studies from our group [ 26 , 27 , 28 ]. The time of the maximum of the first two peaks of s(t) was determined for each impact.…”

Section: Methodsmentioning

confidence: 99%

“…Later, it was also shown that the instrumented hammer could be used to detect a change in the tissue material properties during a rhinoplasty performed in rabbits [ 27 ] using a machine learning algorithm. The method was eventually applied to an anatomical subject showing the feasibility of this approach in a configuration close to that of the operating room [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Using an Instrumented Hammer to Predict the Rupture of Bone Samples Subject to an Osteotomy

Nugues

Rosi

Hériveaux

et al. 2023

Sensors

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Osteotomies are common procedures in maxillofacial and orthopedic surgery. The surgeons still rely on their proprioception to control the progression of the osteotome. Our group has developed an instrumented hammer that was shown to provide information on the biomechanical properties of the tissue located around the osteotome tip. The objective of this study is to determine if this approach may be used to predict the rupture of a bone sample thanks to an instrumented hammer equipped with a force sensor. For each impact, an indicator τ is extracted from the signal corresponding to the variation of the force as a function of time. A linear by part regression analysis is applied to the curve corresponding to the variation of τ as a function of the distance d between the tip of the osteotome and the end of the sample. The experiments were conducted with plywood and bovine trabecular bone samples. The results show that τ starts increasing when the value of d is lower than 2.6 mm on average, which therefore corresponds to a typical threshold detection distance between the osteotome tip and the sample end. These findings open new paths for the development of this instrumented surgical hammer.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using an Instrumented Hammer to Predict the Rupture of Bone Samples Subject to an Osteotomy

Nugues

Rosi

Hériveaux

et al. 2023

Sensors

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“…More recently introduced deep learning (DL) models are specific ML applications whose complex algorithms and neural nets (consisting of many hierarchical layersi.e., deep-of non-linear processing units) train models, with little to no explicit human data input. These progressive developments make AI an incredible tool in various fields, including healthcare, where it has been deemed suitable for repetitive analytic tasks [2], complex calculations [3], and complex forecasts [4,5].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

Bulfamante

Ferella

Miller

et al. 2022

Eur Arch Otorhinolaryngol

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Purpose This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability. Methods MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability. Results Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%. Conclusions AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.

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Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

Pugliese,

Maccari,

Felisati

et al. 2023

Clinical Case Reports

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Key Clinical MessageLarge language models have made artificial intelligence readily available to the general public and potentially have a role in healthcare; however, their use in difficult differential diagnosis is still limited, as demonstrated by a case of necrotizing otitis externa.AbstractThis case report presents a peculiar case of necrotizing otitis externa (NOE) with skull base involvement which proved diagnostically challenging. The initial patient presentation and the imaging performed on the 78‐year‐old patient suggested a neoplastic rhinopharyngeal lesion and only after several unsuccessful biopsies the patient was transferred to our unit. Upon re‐evaluation of the clinical picture, a clinical hypothesis of NOE with skull base erosion was made and confirmed by identifying Pseudomonas aeruginosa in biopsy specimens of skull base bone and external auditory canal skin. Upon diagnosis confirmation, the patient was treated with culture‐oriented long‐term antibiotics with complete resolution of the disease. Given the complex clinical presentation, we chose to submit a posteriori this NOE case to two large language models (LLM) to test their ability to handle difficult differential diagnoses. LLMs are easily approachable artificial intelligence tools that enable human‐like interaction with the user relying upon large information databases for analyzing queries. The LLMs of choice were ChatGPT‐3 and ChatGPT‐4 and they were requested to analyze the case being provided with only objective clinical and imaging data.

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Anatomical subject validation of an instrumented hammer using machine learning for the classification of osteotomy fracture in rhinoplasty

Cited by 9 publications

References 23 publications

Using an Instrumented Hammer to Predict the Rupture of Bone Samples Subject to an Osteotomy

Using an Instrumented Hammer to Predict the Rupture of Bone Samples Subject to an Osteotomy

Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

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