On December 27, the company released news that its lung surgery planning products passed the approval of NMPA Class III medical devices by the State Food and Drug Administration.
This product is used for lung surgery planning, which is the first artificial intelligence application for cancer surgery treatment approved by NMPA Class III certificate in the world, and also the first and only lung surgery planning product approved by NMPA Class III certificate, which can provide a reference for thoracic surgeons to make lung surgery plans.
It is reported that the thoracic surgery AI clinical trial was verified by Peking University People's Hospital, Shanghai Pulmonary Hospital affiliated to Tongji University and Xiangya Second Hospital of Central South University, using multi-center multi-reader multi-case design to verify the effectiveness and safety of the product.
Clinical trial results show that the product can fully realize the three-dimensional reconstruction of pulmonary blood vessels and bronchus in a very short time, providing the relationship between the target lesion and peripheral blood vessels and adjacent tissues, and the test proves that the product can greatly improve the identification and reconstruction accuracy of anatomical structure and variation anatomical structure. As for the part of assisting doctors in preoperative surgical planning, the experiment proved that the accuracy index improved significantly in the correct selection of surgical methods and the accurate resection range of target lesions.
In other words, lung surgical planning AI can effectively improve the time efficiency and accuracy and consistency of preoperative planning, and has obvious advantages in helping the precision surgery of lung cancer.
Since the auxiliary diagnosis, new drug research and development and other scenes gradually into AI, the minimally invasive surgery market with more than 10 million annual operation volume is undoubtedly the next ideal scene for medical AI, and the first AI cancer surgery treatment of three types of syndrome of deducing medical has found a new path for breaking the game in this field.
Medical AI, which has been dormant for a long time, is coming to a market of 10 billion.
A surgeon's most valuable time should be spent operating
With the continuous development of laboratory science, the deepening of pulmonary local anatomy, the continuous innovation of surgical equipment, and the continuous improvement of preoperative planning, the current status and overall treatment framework of lung cancer surgery have been gradually established. From the early stage of lung lobectomy, it has gradually developed into lung segmentectomy and subpulmonary segmentectomy. Under the condition that the healthy tissue of patients is preserved to the maximum extent, It can effectively achieve the purpose of surgical resection and improve the quality of life of patients.
The results of the JCOG 0802 study, published in the Lancet in 2022, showed that segmental resection of the lung will lead to better outcomes for patients with eligible tumors.
However, all kinds of lung cancer precision removal surgery also has its pain points. In order to ensure that the lesion can be completely removed, the preoperative doctor must use the patient's lung image for modeling. The traditional preoperative plan relies on manual processing, which requires the doctor's reconstruction level to be high, and is susceptible to personal experience and subjective prior knowledge. The speed is slow and the efficiency is low, which greatly reduces the surgical efficiency of the hospital. More importantly, the traditional preoperative plan relies on enhanced chest CT scan, and the incidence of anaphylaxis with contrast media is about 3.8-12.7%. Once anaphylaxis occurs, anaphylactic shock may even occur, which invisibly increases the risk of patients during the examination.
On the other hand, there is a high probability of variation in the blood vessels, organs and bronchus of the subpulmonary lobes of lung cancer patients, and the doctor only knows how to cut at the moment of opening the chest cavity, which is a great test of the doctor's surgical experience and decision-making ability.
Therefore, the lung surgery planning AI approved by the Reasoning Medical is to solve the core difficulties in the above two lung cancer surgeries. Relevant data show that the AI can complete the three-dimensional reconstruction of CT images in a few minutes, and give the analysis results based on the images, and the product can complete the extraction and accurate reconstruction of lung segments based on plain scan CT, which is suitable for a wider range of people. At the same time, the AI can also provide multiple surgical paths for doctors to navigate based on the variation of patients' tissues, minimizing the probability of accidental injury and complications under insufficient time decisions.
In the view of Chen Kuan, founder and chairman of Inferi Medical, surgeons' most precious time should be used in surgery, and those supporting the operation, such as manual drawing, consume a lot of time for doctors, Inferi hopes to use AI to help doctors efficiently automate.
From the perspective of surgeons, lung surgery planning AI brings not only efficiency improvement, but also cost and compliance control. In practice, if doctors complete 3D reconstruction through out-of-hospital institutions, there is a risk of compliance, and it takes 1-2 days to obtain results. Now with AI capabilities, they can rebuild themselves in the hospital, leaving the data safely and standardized in the hospital, which greatly improves the efficiency and reduces the cost of reconstruction. At the same time, support doctors to use 3D reconstruction related cases to do scientific research and publish papers.
From the beginning of product development, we have worked closely with top hospitals and clinical experts at home and abroad to deeply understand surgical clinical problems and pain points and determine the direction of product development. In the process of product development, on the one hand, artificial intelligence algorithms are trained through a large amount of clinical data, and on the other hand, in-depth communication with clinical experts is carried out to integrate the experience of clinical experts into artificial intelligence algorithms and constantly iterate and upgrade.
The real meaning is to open up the whole process of "sieve - diagnosis - treatment - tube - research"
For inferential medicine, the application of artificial intelligence from assisted diagnosis to assisted treatment is both unexpected and reasonable.
What was not expected was the deep technical accumulation of Inferi in the surgical field. The vast majority of AI companies have failed to make breakthroughs in the field of surgery, not that they have not seen the demand, but that it is difficult to overcome the technical problems in the process. In order to achieve adjuvant therapy in the most complex surgical procedures, AI cannot just look at the general, but must clarify the individual blood vessels within a specified time, accurately find the edge of the lesion, and can analyze every type of variation in the surrounding.
Chen Kuan believes that the successful development of lung surgery planning AI is inseparable from two points. First, the long-term accumulation of technology, can carry out accurate and efficient analysis and processing of medical images, as early as last year, Inferiao has won the "pulmonary artery segmentation" and "trachea and bronchial segmentation" in the top international conference of medical images MICCAI two important competitions, in this direction, Inferiao has been in the international leading position, is expected to lead the future development of this technology. The second is the in-depth cooperation with the hospital, which can accurately understand the needs, realize the needs, and constantly improve in the practice process.
What makes sense is the inferential product matrix expansion strategy. In the past, medical AI preferred to broaden horizontally, and was good at constantly empowering new diseases to achieve multi-application coverage of a single department. However, in order to jump from one department to all departments, medical AI must take a vertical extension to achieve the full process of screening, diagnosis and treatment of a single disease.
In addition, the AI-assisted diagnosis track is already the Red Sea, to open up new market space, break its own ceiling, and assume that medical treatment must break through. Infertile already has high barriers and technical reserves in the image processing of lung diseases, and intelligent radiotherapy planning software has verified the feasibility of the commercialization of AI tumor track, and lung surgery planning AI has naturally become the next choice for Infertile medical.
In the past, Think medical, like most medical AI companies, adopted a two-way method to broaden the types of diseases horizontally and open up the whole process of diagnosis and treatment vertically. However, in reality, the "treatment" in the whole process of medical AI enterprises is superficial, and there is no real sense of opening up the process.
Until now, the medical lung surgery planning AI has passed the approval, and the "horizontal and vertical" of medical treatment has finally been completed, and the longitudinal "screening, diagnosis, treatment, management, and research" can really run through and take the lead in running through. As an innovative tool for the precision treatment of lung cancer, lung surgery planning products are an important part of the clinical cycle of "screening, diagnosis, treatment, management and research". Moreover, Inferthink has become the world's first AI medical high-tech company with three types of lung cancer screening, diagnosis and treatment at the same time, and with all three types of solutions for the whole cycle of lung cancer, Inferthink AI fully enables the integration of accurate diagnosis and treatment of the whole cycle of lung cancer. The successful approval means that lung nodules have evolved from the 1.0 era of large-scale early screening and the 2.0 era of precision diagnosis to the 3.0 era of precision diagnosis and treatment.
In this regard, Chen Kuan said that the approval of the imaginative lung surgery planning product strengthens our commitment to China's AI innovation power leading global innovation, and provides thoracic surgeons and patients with a more efficient, more accurate and safer new choice for lung cancer diagnosis and treatment. The AI healthcare industry is working hard to turn some of the most promising scientific breakthroughs into high-value treatments for the most pressing and intractable medical challenges, such as lung, liver and biliary, and urinary cancers. The idea is to resolutely enter the strategy of treatment horizontally, vertically and vertically to provide support for the development of medical technology means for major diseases and improve the health of all mankind.
Construct a surgical treatment ecology of tumor under AI
About two years ago, Infervision Medical proposed the concept of AI-4D intelligent surgical planning system, aiming to establish InferOperate, a tumor treatment ecology based on image artificial intelligence, to carry out automatic reconstruction of organs and tissues such as lungs, liver, kidney and bone according to CT image data.
In other words, the just-certified lung surgery planning AI is just the "first shot" in the surgical field, and more research and development pipelines for tumor treatment AI will "follow". According to InferOperate Medical, some of these products are still in development, others have advanced to clinical trials, and more Inferoperate AI-4D products are coming.
In the field of cancer treatment, deduce the depth of medical layout, and the full picture of its "cancer precision treatment puzzle" has been slowly developed, including surgical planning products, AI surgical robots and follow-up management of early lung cancer patients. In July this year, the research and development of the medical AI surgical robot "Dragon's Eye ® puncture surgical robot" also achieved a major breakthrough, entering the precision treatment, and deeply moving into the clinical treatment field.
Clinically, the percutaneous penetration surgical robot is used to assist percutaneous radiofrequency ablation for the treatment of patients with poor cardiopulmonary function, older age, or who are unable to choose endoscopic surgery. At the same time, we also carry out in-depth research in the field of patient follow-up, and the whole-process management solution of lung nodules can find and manage more early-stage lung cancer patients, observe and compare the whole-process treatment of patients, and effectively popularize science and prompt patients after discharge.
From the perspective of layout logic, all surgical AI pipelines of Inferi Medical meet the two-way requirements of market and policy. On the one hand, the operation volume of the relevant surgical operations involved by the company exceeds one million, and the demand is large. On the other hand, the introduction of precision medicine related policies has been transmitted from national policies to the local level, providing continuous power for the precision and intelligence of surgical surgery.
So, under the background of high-quality market, can the medical breakthrough be replicated? The answer may not be so easy.
After the first certificate of tumor surgery AI, the medical AI market is bound to have more similar AI applications in the next few years. However, the commercialization advantages brought by the first can not be underestimated, and the breakthrough of conventional medical AI decision-making thinking, innovative thinking, and innovative ability are difficult to repeat.
Looking back on the past 7 years of deductive medical entrepreneurship, from the industry's first pulmonary nodule AI Class III certificate to the industry's first pulmonary surgery planning AI Class III certificate, Deductive Medical victory can also complete daring innovation after in-depth understanding of medical treatment. This may be the key to thinking about the way medicine has come to today.
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